And similarly, we find that certain languages also require context when we want to choose the appropriate verb form.

类似地，我们发现某些语言在选择适当的词汇形式时也需要语境。


We also check the feasibility of each strategy for annotation quality and costs to annotators.

我们还检查了每种策略的可行性，包括注释质量和成本对注释者：


And as a researcher, positionality can influence the research process and its outcomes and results because it can change the decisions that researchers make.

作为研究者，位置性可以影响研究过程及其结果，因为它可以改变研究者做出的决定。


我们的框架在两个主要步骤中工作：


After the first step, we have all the right tokens, but they're not ordered.

第一步之后，我们有所有的正确令牌，但它们没有被订购


For testing, we reserve the entire common sense reasoning group for testing, and we select additional 5 tasks from VQ and Miscellaneous groups.

采样。我们保留了整个常识推理组用于测试，并且我们从Wiki和MiscellaneousGroup中选择了另外五个任务。


So for GPT 4, in the social acceptability task, we find that it's most aligned to people with a college education or Graduate School education and we find the same for Dynahate where it's most aligned to people with a college education.

因此，在社会可接受性任务中，我们发现GPT-4与大学教育或研究生教育最相关，而我们发现同样的事情发生在DALL-E中，它与大学教育最相关。


Additionally, at the time of our research, we discovered a considerable discrepancy in the availability of instructional datasets between NLP and multi-modal.

此外，在我们研究的过程中，我们发现了一个相当大的差异在LAP和多模之间的指令数据集的可用性。目前


In total, we generate 55,000 specific goals with scripts.

总共，我们生成了55,000个与脚本相关的特定目标，


When trained on KITMUS, however, both C2F and BERT4Coref perform significantly better than the random choice.

然而，在基德姆斯上训练，我们的两种模式都比随机选择表现得更好。


But Chinese is missing and lack of coverage on certain meaning representation.

国语缺失。莱克人可以勇敢地承受不确定的许多代


因此，我们也想在讨论中强调一下，


We also validate the covertness of the provided embedding by visualising the embedding of sentences on four dataset [INAUDIBLE 4:39] PCA.

我们还通过将句子嵌入到预训练的BERT模型中来验证了提供的嵌入的隐私性。


We do this through our framework NLPositionality.

我们通过我们的框架nl位置性来做到这一点。


Creating the dataset is an essential step to this end.

创建数据集是实现目标的必不可少一步。


And the other is to do NLP research with the lens of perspectivism.

要对研究过程进行NLP研究。


We then fine-tuned over 20 models on CoNLL-2003.

然后，我们在Cora2003上对超过20个模型进行了微调。


So we did a series of analysis where we tried to perturb the input sentence by, trying to preserve the relevant structure but adding like noise to the input.

我们做了一系列的分析，试图通过保留相关结构来破坏输入句子，但添加噪音到输入，并在执


So we can do the same thing by choosing unacceptable sentences from the same matching, and that could also be used to test the models acceptability.

我们可以通过从同一匹配中选择不可接受的句子来做同样的事情，这也可以用来测试模型的可接受性。


We found it is because most of the major natural languages can obtain performance gain, except that English performance drops in seven datasets and only gains in three datasets.

发现的原因是大多数主要自然语言都能获得性能增强，除了英语在七个数据集中下降，只在三个数据集中上升。


Use only one model for every latency regime and handle latency through specific parameters.

单个模型来处理每个延迟模式，并通过特定的参数来处理延迟。


So this has sound the alarm for us to acknowledge and tackle the fairness issues resulting by language model political leanings.

所以，这个声音警告我们要认识并解决由于语言模式政治表达方式而产生的不公正问题。


Results in the figure show that the semantic completeness in generated scripts is acceptable but the faithfulness to the constraints cannot be guaranteed.

图表的结果表明，生成的脚本的语义完整性是可接受的，但对约束的忠诚是无法保证的。


We compare this to the other state-of-the-art AL strategies that are commonly used in the community.

我们将其与其他艺术艾尔策略相比，这些策略在社区中通常使用。


Here are some examples of indirect references for example, "the newer one" or "the song that's not energetic."

例如，例如，较新的或非能量符号的歌曲。


Where A and B are samples from Wikipedia.

A和B是来自维基百科的样本。


After the first step, we have all the right tokens, but they're not ordered.

第一步后，我们已经有了所有正确的token，但它们并没有被正确排序。


So in this work, we revisit the minimal pair paradigms.

在这项工作中，我们重新访问最小对的范畴。因此，


Without task-specific training on KITMUS, both models do not perform well.

结果。在任务特定训练的KidMus中。这两个模型都没有表现出很好的效果。


While dissonance is a very common phenomenon we experienced in daily decision making, they are really rare to find expressed in language among other kinds of discourse relations.

虽然不一致是我们在日常决策中经常遇到的现象，但在其他类型的言语关系中表达不一致的现象却很少见。


First of all, the alignment between input and output is not given in the training data.

首先，输入和输出之间的对齐在训练数据中没有给出；


The argument is based on the principle of dependency length minimization that I will explain on the basis of these examples.

这个论点是基于依赖关系长度最小化原则，这些例子就是基


In our experiments, we settled for a 5-shot prompting strategy where we just marked each sentence that we provide to the system, with the language it's in.

的实验中，我们选择了一种五发促进策略，只是用系统中的语言标记我们提供给系统的每个句子。


Second, there's a "Background-Both" setting, where background knowledge is available both at pretrain time and inference time.

其次，有两种背景设置：在练习前和练习时都有


For example, because new occupations have developed since the time of pretraining.

例如，因为新职业自从预训练以来已经发展起来。


Or the pronunciations are too similar to each other and hard to disambiguate.

或者发音太相似，难以区分，


Language model acceptability judgments are not always robust to context.

语言模型可接受性判断并不总是对上下文有坚实的依据。


Then the provider requests the embeddings from the stealer's service with the data set.

然后，提供商要求从类似的服务中获取数据集的嵌入。


And it's incredibly hard to determine what is actually neutral and should be retaining language monitoring data.

而且很难确定什么实际上是中立的，并且应该保留语言监控数据，因此它


And finally, we use our benchmark as well as other metrics to evaluate different models on the document-level machine translation.

最后，我们使用我们的基准值（与其他矩阵一样）来评估文档级计算机传输中的不同模型。


However, previous studies do not enable planning for specific goals and manual dataset annotation is expensive.

然而，以前的研究无法为特定目标制定计划，而且手动注释数据集是很昂贵的。


These tasks are derived from 21 existing open-source dataset and each task is equipped with five expert written instructions.

这些任务是从现有的开源数据集中衍生出来的，每个任务都配备了五个预先编写的指令。


Where A and B are samples from Wikipedia.

A和B是来自维基百科的示例。


To no surprise, the classifier performed not much better than chance.

毫不奇怪，分类器的表现不比随机好多少。


I'd like to begin with a brief introduction to weak supervision and weakly supervised learning.


And this helps us identify cases like the one here, where in Chinese you need context to translate proper nouns to make sure that you're using the same translation within the document.

这有助于识别像你这里的案例：在中国，我们需要联系传递正式通知，以确保您在文档中使用相同的传输。


Then we will present the main contribution of our article.

然后我们将介绍我们文章的主要贡献


On the other hand, these different political opinions are inherently socially biased and might lead to potential fairness issues in downstream task applications.

另一方面，这些不同的政治观点在社会上是有偏见的，并且可能导致潜在的公平性问题。


In this figure, the blue line is Cross-lingual Few-shot transfer.

在这个图中，蓝色线是跨语言的虚拟传输，


We host 2 tasks on lab in the wild, one of them being social acceptability, and the way this works is that participants will read a situation from the social chemistry dataset and, then they'll write how socially acceptable a situation is.

我们在现实世界中进行了两个任务，一个是社会可接受性，方式是参与者从社会化学数据集中读取情况，然后他们会评估情况的社会可接受性。


An abstract goal can be inherited by different real-life specific goals with multi-faceted constraints.

一个抽象的目标可以由具有多面约束的不同现实生活目标继承;


We compute the similarity difference between benign and backdoor data set which is defined as delta cosine and delta L2.

我们计算了Benign和后门数据集之间的相似性差异，这被定义为Deltacosin和DeltaL2。


Hello everyone, my name is Yusen Zhang from the Penn State University.

大家好，我是宾夕法尼亚大学的UsmanJohn。


Otherwise, there is a large performance drop.


We can then also note that different languages have different proportions of these discourse phenomena.

然后我们还可以注意到，不同语言中这些话语现象的比例是不同的。


These are the links to our core data set and our paper.

这些是连接到我们的代码数据集和纸张的链接。感谢您与我们联系。


As can be seen here, dissonance was only found in 3.5% of the annotated pairs.

如图所示，仅有3.5%的对齐对出现了不一致。


And some people have suggested targeted evaluation on context-dependent translations, but these resources only support limited types of context-dependent translations and limited sets of languages since they usually rely on domain knowledge and human curation.

并且一些人已经建议针对上下文依赖翻译进行目标评估，但这些资源只支持有限的上下文依赖翻译类型和有限的语言集，因为它们通常依赖于领域知识和人类创作。


Existing works can be broadly classified into four categories.

现有的作品可以大致分为四类。


Specific architectures are usually trained, introducing additional modules to be optimized.

通常需要额外的模块来优化，


If the language model has access to some partially overlapping background knowledge, then the accuracy is between 82 to 87%, which is more realistic.

如果语言模型可以访问一些部分重叠的背景知识，那么精度在82%到87%之间，这更具现实意义，


As usual, we have a training set of utterances.

样：通常，我们在这种情况下会训练自己


We then replicate a very similar setup for the toxicity and hate speech detection task, where they'll read an instance from Dynahate and write whether they think it's instance of hate speech.

然后我们重复了一个非常相似的设置用于毒害和仇恨言论检测任务，其中他们读取一个实例并写出他们是否认为这是仇恨言论的实例。


We find that on transferring the zero-shot performance on the annotated data set is already much better than chance with the best, with AUC .62.

我们发现在传输注解数据集上的零短期表现已经远远好于机会，使用AUC点6.2的最佳结果。


"Marge read this absolutely fascinating book about bees yesterday."

这两个句子都很好。马克·雷德写了关于昨天的比斯塞斯的绝对迷人的书。但也可以说马格读了昨天这本关于蜜蜂的绝对迷人的书。所


However, if we allow to continue fine-tuning on the clean samples, then FTw performs equally well as other methods.

然而，如果我们被允许继续对清晰样本进行精细调节，那么FTW的性能将与其他方法一样好。


These models are compared to six baseline models which are CamemBERT OSCAR 138 GB, CamemBERT OSCAR 4 GB, CamemBERT CCNET 4 GB, PubMedBERT, BioBERT, and ClinicalBERT.


In summary, we establish the constrained language planning problem.

总之，我们建立了受限语言规划问题，


And finally, we look at different individual tokens that have high P-CXMI.

最后，我们看到了不同的个体标记具有高位的pxmi，并允


These utterances are paired with logical forms that represent core aspects of their meaning.

这些先驱者，带着逻辑的形式，代表了他们的思想。


For each of the five discourse phenomena we identified, we create taggers to automatically identify words that pertain to the phenomenon.

对于我们识别的每一个五个现象，我们都创建了标志来自动识别与该现象相关的单词，并称


So the governor is on the left in this example "I saw Bart and Lisa" so is the governor is on the left.

在这个例子中，政府在左侧（isobutyl）和左侧（lisa），所以政府在左侧（absent）


A good planner should write scripts that are reasonable and faithful to constraints.

一个好的规划者应该写出遵循约束的合理和可靠的脚本。


Thus, this is the model that we use to cold start the active learning.

移学习对CE任务的迁移学习后，通过进一步对迁移学习进行迁移学习，我们发现迁移学习对CE任务的迁移学习后，通过进一步对迁移学习进行迁移学习，我们发现�


The second part is marked words, which is a method to identify the words that distinguish marked groups from unmarked ones, which I'll elaborate on shortly.

第二部分是标记词，这是一种方法，用于识别区分标记组和未标记组的词。


In the first bubble, Bob says, "Remember that song we were listening to yesterday?"

在第一个泡沫中，Bob说：“记住我们昨天听到的那首歌”


So what we do is that we show some background knowledge about the two entities.

我们做的是展示一些关于20世纪的背景知识；


And second, how well do models handle these cases?

其次，模型如何处理这些情况？


Before these main steps, we first select a trigger set.

在这些主要步骤之前，我们首先选择一个触发器集；


So overall, we propose the first large scale multi-model instruction tuning dataset with significantly improved their short capability of OFA, and we explore different transfer learning technique and show their benefits.

因此，我们提出了第一个大规模的多模态指令调节数据集，我们显著提高了OFA的实时能力，我们探索了不同的转移学习技术，并展示了它们的好处，


So that is what we call as the mismatch scenario.

此我们称之为不匹配场景。所以在


For example, we put the German, English, Chinese queries together to train a multilingual model.

例如，我们把德语、英语和中文的查询放在一起训练一个多语言模型，


大家好，我是Zhuang，


We find this in the GPT 4 social acceptability task as well as the Dynahate task analysis as well.

我们在社会可接受性任务（GPT-4）中发现了这一点。同样地，DINIE也可以用于任务分析。


The first one is the model architecture.


And during inference we can use this model to translate German queries or Chinese queries, et cetera.

在推理时，我们也可以使用这个模型。我想问一下，欧盟委员会是否有任何关于此事的信息。


We find that Dynahate is also most aligned to English speaking countries.

我们发现DINAHET也是最匹配的英语国家。”


All the pre-trained model obtained from NACHOS are freely available on Hugging Face, and under the MIT license, and all the training scripts are on our GitHub repository.

从Nachiós获得的所有预训练模型都可以在Yugenface上免费获得，并且所有的训练脚本都在我们的GitHub存储库上。所以


We find that PRC has the highest percentage of dissonance and works best for rare class.

我们发现PRC有最高的不一致百分比，并且适用于罕见类别，但


It's okay the way instead of "it", we have this long NP.


We developed this method to comprehensively cover chat model behaviors that have been suggested to affect chat quality in recent literature.

我们开发了这种方法来全面覆盖聊天模型行为，这些行为被认为会影响聊天质量的研究中提出的。


In this work, we present the first systematic study of large language model prompting for machine translation.

在这项工作中，我们提出了机器翻译中大语言模型提示的第一个系统研究。


For the first time, we show strong generalization to deeper recursion without relying on trees.

我们会展示强大的泛化以避免在纱线上滴落。


Conceptually, our permutation model works roughly like this.

从概念上讲，我们的变换模型大致像这样工作。


The AltEntities Corpus has 6,000 alternative questions across three domains, and it has 42,000 indirect referring expressions.

AlternatingCorpus在三个领域中有6,000个替代问题，并且有42,000个间接引用表达式。


Second, WSL approaches should be compared with few-shot learning baselines, as both work on clean samples.

其次，WLS方法应该与未来学习的基线相比较；


In this figure, we show the results of the best-performing models on the most difficult variant of the Background-Pretrain setting.

在此图中，我们显示了背景预训练设置中最难的变体中表现最好的模型的


So what we do is that we show some background knowledge about the two entities.

因此，我们展示了关于这两个实体的背景知识。


And so we opt to re annotate data to get many annotates for instance and to get a rich set of demographic data.

因此，我们选择重新注册数据，以便获得许多实体，例如人口统计数据。


We use state-of-the-art, neural MT metrics, and additionally also show expert-based human evaluation results.

我们使用最新的神经MT评估指标，并且还展示了基于专家的人类评估结果。


If the task is a multi-model classification task, we report accuracy.

如果任务是多模态分类任务，我们报告准确率；


So we get dependencies from the governor.

昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了


So in practice, there's no reason to choose more complex WSL methods which require more computation time and disk space.

因此，在实践中，没有理由选择更复杂的WSL方法，这些方法需要更多的计算时间和磁盘空间。


Here loves to all conjuncts separately: Lisa, Bart, and Maggie.

这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了


What we see here is that when the governor is on the left, the tendency for the left conjunct to be shorter grows steadily, with the absolute difference in words, and the same is observed when there is no governor as in coordination of sentences.

我们说的是什么？那就是当总督在左边时左翼结合的倾向是逐渐增长的，词语之间绝对不同，当没有总理参与协调成语时也会观察到这一现象，


At the same time, if we do observe poor generalization, what causes the performance drop of these models?

同时，如果我们观察到一般化不佳，这会导致这些模型的性能下降的原因是什么？


First, is clean validation data necessary for WSL or can we maybe use a noisy validation set instead?

首先，是否需要清洁的验证数据来验证WSL，或者我们可以使用一个嘈杂的验证集代替吗？


Our permutation method is very flexible, but it brings the challenge that finding the highest-scoring permutation is NP-hard.

我们的变换方法非常灵活，但它带来了一个挑战：找到最高得分变换很难，


This connects to an archetype that people have called the "Strong Black Women" archetype.

这与人们所称的“强大黑人女性”典型而且听


And leverage the knowledge already acquired by the model through the attention mechanism between audio input and textual output.

通过注意力机制在音频输入和文本输出之间的注意力机制，已经获得了所需的知识。


First, to use already existing offline ST models without re-training or adopting specific architecture for SimulST.

首先，使用现有的离线ST模型，不需要重新训练或采用特定架构来适应CST；使用


Next, to improve the number of dissonance examples, we use a Probability-of-Rare-Class strategy — PRC — to select mostly the examples that are highly likely to be descended by the current model at any round of rare.

接下来，为了提高不一致示例的数量，我们使用概率稀疏类策略（PRC）来选择大多数示例，高度可能在任何一轮的AL中与当前模型不一致。


我们的演讲也包括了这一点。


Thus, this is the model that we use to cold start the active learning.

因此这是我们用来启动主动学习的模型。


If you want to discover more results, read our paper.

如果你想了解更多结果，请阅读我们的论文，


And with that, Bob sets the dialogue context.

，并通过这句话设定了对话背景。


So based on these patterns, we conclude with three recommendations for model owners.

基于这些模式，我们可以得出三条模型所有者的建议


If we directly train neural networks on weakly labeled data, the neural networks tend to memorize the label noise and do not generalize.

在弱监督中，我们不会手动标记数据，而是使用弱标签源来标记数据，例如简单的规则、知识库或低质量的引语源，如右图所示。


Without task-specific training on KITMUS, both models do not perform well.

结果。在基德姆斯上进行任务特定的训练，然而，两种模式都没有表现出色；


We evaluate constrained language planning ability of large language models and develop an over-generate-then-filter method for large language models.

我们评估了大型语言模型的受限语言规划能力，并开发了大型语言模型的过度生成滤波器方法。


However, previous studies do not enable planning for specific goals and manual dataset annotation is expensive.

然而，之前的研究无法为特定目标进行规划，并且手动数据集注释是昂贵的。


And while it sounds positive at first glance, there's been work showing that this kind of archetype actually is very harmful because it puts a lot of pressure on these demographics to be resilient and strong against societal obstacles.

第一眼看起来似乎是积极的，但实际上工作已经表明，这种架构类型实际上是非常有害的，因为它会给这些人施加很多压力，要求他们要坚韧和强大，抵抗社会障碍


If we look at the main results of EDAtt, we'll plot the simultaneous speech translation results on graphs in which we have BLEU on one side that measures the translation quality, and average lagging that is the latency measure, and we also consider the computational aware average lagging that accounts for the model's computational times to predict the output.

如果你看一下那件事的主要结果。我们将在图表上将同时翻译结果打印出来，其中一边是蓝色，用于衡量翻译质量和平均延迟。这就是延迟测量，我们还考虑计算机意识的平均缺失，计算机模型计算时间预测输出。


For example, the same genre or the same artist for a song.

例如同一类型或同一艺术家等。


That's why in the second step we use another model to predict a permutation to put them into the right order.

因此，在第二步，我们使用另一个模型来预测一个置换来将它们放入正确的顺序。


We would also like to highlight that we expose the unique dilemma regarding language model political biases.

我们暴露了语言模型政治偏见的独特困境：


And show that large language models can effectively decompose goals into steps.

，并且表明大型语言模型可以有效地将目标分解为步骤。


所以为什么会有这种问题？


For example, if we receive a speech chunk containing "I'm going to talk about..." and our model predicts the translation in German, and we will look at the cross-attention weights, we'll see that the first two words points to the earliest received speech frames, while the last word points to the last received speech frames, as lambda speech frames.

例如，如果我们收到一段语音片段，内容是“我要谈论的”，而我们的模型预测翻译成德语。我们将看待跨注意力等待我们看到前两个单词指的是最早接收到的语音框架，而最后一个单词指的是最后接收到的语音框架（至少是lamda语音框架）。


We train on one source language and transfer to another language.

在一个源语言之间传输和传输到另一个语言之间。因此，


For example, training a model with an average of one second latency and another one with two seconds latency, and so on.

，其平均延迟为1秒钟，另一个模型的平均延迟为2秒钟，等等。


最后，我们有了开放源代码；


So now we're better equipped to answer who do NLP datasets and models align with the most.

虽然我们现在更好地回答了NLP数据集和模型与哪些国家最匹配的问题，但


For example, you can see how measuring the proportion of turns with self and partner contradictions explains 5% and 10% of conversation quality, respectively, while the average Likert consistency scores explain only 4% or less.

例如，您可以看到如何测量自我和配偶的对立性与对话质量的比例，分别解释了5%和10%的对话质量，而平均的Lickert一致性得分只解释了4%或更少。


And we compare with popular strategies that are also applied to offline models that are the Wait-k strategy and the Local Agreement.

我们将我们的方法与适用于离线模型的适当策略进行比较，例如Whitkey策略和局部一致性策略，并且我们还将


We provide the first and second speech bubbles automatically, but the third one is filled in by the annotator.

我们自动提供第一个和第二个语音泡，但第三个语音泡由注释者填充。


With CoScript we can try smaller but specialized models for constrained language planning.

以使用Corescript训练更小但更专门的模型来进行受限语言规划。


Here we present MultiInstruct, the first multi-modal instruction tuning benchmark dataset that consists of 62 diverse multi-modal tasks covering 10 broad categories.

在这里，我们展示了MultiInstruct，第一个多模态指令调整基准数据集，它由62个不同的多模态任务组成，涵盖10个类别。


We use one instruction versus 5 instruction.

我们使用一条指令对五条指令进行比较，


So overall, we propose the first large scale multi-model instruction tuning dataset with significantly improved their short capability of OFA, and we explore different transfer learning technique and show their benefits.

总之，我们提出了一个用于大规模多模数指令调谐数据集，显然可以显著提高OFV的射程，并探索不同的传输学习技术，并展示其优势。


This could happen when the user cannot remember the name of the song.

这会发生在用户无法记住歌曲名称时。


我们总是使用一个简单的模板，


Protecting the copyright of large language models for embedding as services via backdoor watermark.

保护大语言模型用于嵌入和服务的版权：通过后门水印让我们首


We use all the instances in the test split for each task.

我们使用每个任务的所有示例进行测试。


Our conclusion is that, for good generalization we would need a better model architecture, larger model size, as well as more fine tuning examples.

我们的结论是：为了良好的泛化，我们需要更好的模型架构、更大的模型尺寸以及更多的精细调节示例。


So these two trees only show the length of the crucial dependencies, the ones that are not constant among these two structures.

这两棵树只显示了关键依赖关系的长度，这些关系在这两种结构中是不恒定的。


In addition, ABC-Eval labels are more predictive of the overall conversation quality compared to metrics produced by existing methods, as shown by this simple linear regression analysis.

此外，ABC-EV的标签比由现有方法产生的矩阵更能预测整体通话质量，正如通过这种简单的线性降低分析所显示的。


But if you'd like to learn more, feel free to check out our dashboard for the most updated analysis results and our paper.

所以如果您想学习更多，请自由查看我们的桌子，查看最先进的分析结果和我们的论文。


The first one is the model architecture.


But that's not really the case for Aditya Sharma.

但这并不是阿迪蒂亚·沙玛的真正情况，因为我们对攻击


And second, how well do models handle these cases?

其次，模型如何处理这些情况？


That's why in the second step we use another model to predict a permutation to put them into the right order.

这就是为什么在第二步中，我们使用另一种模型来预测混血，以将它们放入正确的顺序。


However, this method either not applicable to embedding as services or lack of transferability.

然而，这些方法既不能用于嵌入广告服务，也不能用于缺乏可传输性。


In contrast to standard machine learning evaluation, the test set does not come from the same distribution but contains structurally unseen logical forms.

与标准机器学习评估不同，这个测试集并不是来自相同的分布，而是包含结构化的非逻辑形式。


So for example, in the universal dependencies, the structure of the coordination, Lisa, Bart, and Maggie, such that the first conjunct is the head of the whole coordinate structure.

所以，我们从头到尾都有依赖。最后，这也是一种多头方法的方法（即在Transformer中使用多个头），例如在Katz的词法语法中，所有的连接都是头的结构，所以我们可以从总督那里得到依赖性，依赖性是关于和做的。现在，目标论文是为了产生一个新的论点，即（）对比了这些结构的协调性


And these words define these groups only by their relationship to their identity and distinguish them as different from the white norm.

这些词与其身份的关系来定义这些组，并将它们区分开来，区别于白人规范。


For example, in the sentence, "John saw the newly elected president on TV."

例如，在这句话中，约翰看到电视上刚刚当选的总统。


And we ought to do this over looking at the demographics of original data sets annotators, because, usually only a few annotators annotate each instance and because demographics are rarely collected and shared.

我们可以通过查看原始数据集的人口统计图来做到这一点，因为通常只有少数注释者注释过每个实例，而且因为人口统计图确实是真正收集和共享的。


This work is a collaboration between McGill University, Mila, and Microsoft Research.


This is a data set that we collected from Reuters News from 2020, and then annotated them with the same CoNLL-2003 annotation guidelines.

这是我们从2002年收集的数据集，然后用同样的卡诺尔2003年注释指南注释它们。


The conjunction headed approach assumed in Prague dependency treebanks, where coordinate structures are headed by the conjunction.

，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我


As we can see, using more instructions can improve the model's overall performance and reduce its sensitivity a lot.

正如我们所看到的，使用更多的指令可以提高模型的整体性能，并且可以大大降低其敏感性。


We have defined three settings of KITMUS.

我们定义了三个孩子的设置。


Hello everyone, my name is Ying and my colleague Zhiyang and I will be presenting our research on MultiInstruct improving Multi-Modal Zero-Shot Learning via Instruction Tuning.

大家好，我是Yin，我和我的同事Jiayan将介绍我们关于多指令的研究：通过指令调节多模式性性学习。因此，


A similar approach is assumed in Igor Mel'čuk's meaning text theory, where again, the whole coordinate structure is headed by the first conjuct.

尔丘克在他的著作中提到了这种方法，称之为“全连接结构由第一个连接组头部控制”，因此


It's the examples that carry most of the weight.

它的例子承载了大部分的重量。哇！


First, report the model selection criteria.

首先，报告模型选择标准，


In each experiment, we report the min and max performance and the standard deviation of the performance across all 5 experiments.

大值的性能。并且在这五个实验中，性能的标准差。


We find this in the GPT 4 social acceptability task as well as the Dynahate task analysis as well.

我们在GPD的四个社会可接受性任务中发现了这一点。


So we divide pretraining corpora, into pre 45th president of the United States and after 45th president of the United States.

因此，我们将预训练的语料库分为两部分：在45年前美国总统之前和在45年前美国总统之后，


Feel free to get in touch with us if you have any questions.

如果你有任何问题，请随时与我们联系。


You might turn towards a popular API like Prospective API for toxicity detection, and this works really well if you're Carl Jones.

你可能会转向一个流行的API，例如PerspectiveAPI，用于毒性检测，这确实很好用，尤其是当你是卡尔·琼斯时，Pe


So now we use our findings from our analysis to design a benchmark for document-level translation.

解决。现在我们使用我们的分析结果来设计文档的新版本的转换。


Copyright verification is to detect whether a model behind another service contains the word mark.

版权验证是检测一个服务后面的模型是否包含版权内容。水印


Now those are asymmetric approaches to coordinate structures, such as the Prague approach.

现在，人们也采用对称的方法来协调结构，例如，


These utterances are paired with logical forms that represent core aspects of their meaning.

这些表述与表示其含义核心方面的逻辑形式配对。


The first one is the model architecture.


It's absent in the second example "Homer came and sneezed."


And finally, we conclude about the experiments and give you more details about how to access those models.

上展示我们的结果，并最终总结关于实验的内容，并给您更多关于如何访问模型的细节。


But when the governor is on the right this tendency disappears.

来越短，越来越短，越来越短，越来越短，越来越短


Since no dataset of specific goals exists to support our study, we have to acquire these goals first.

由于没有相关数据集来支持我们的研究，我们必须首先获取这些数据。


The summary of our experimental results is that the example quality is more important than the similarity to the source sentence.

我们的实验结果的总结是，样本质量比与源句子的相似度更重要。


And we compare with popular strategies that are also applied to offline models that are the Wait-k strategy and the Local Agreement.

我们与适当的策略进行比较，这些策略也适用于在线模型，例如惠特基策略和本地协议，并且


We determine the third token in the output in a similar way by jumping to another multiset token.

我们通过跳转到另一个多元集令牌来在输出中以类似的方式确定第三个令牌。


Finally, as a conclusion our proper system offered better performance on nine of the 11 downstream tasks and surpassed globally the result of the generic model, here CamemBERT.

最后，作为一个结论，我们的正确系统在11个Don'treams任务中表现出更好的性能，全球范围内的结果都超过了这里的通用模型。


This works well, but trees are usually not given and need to be obtained somehow.

这很好地工作，但树通常不会给出，需要通过某种方式获得。


We also observe that using more data translated to better performance.

我们还观察到使用更多数据可以转换为更好的性能。


You can see how the combination of all ABC-Eval metrics explains over 25% of conversation quality, and as you remove the metrics one at a time, most of them result in losing a decent amount of information about the quality.

可以看到所有ABC-EVA指标的组合解释了超过25%的对话质量，并且当你逐一移除这些指标时，大多数指标都会导致失去大量关于质量的信息。


Please read our paper for more details of our experiments.

请阅读我们的论文以获取更多实验细节。


I think that's pretty much all I have for today.

我想那很棒，我想那是很多的——我已经


On collecting around 1,000 examples of discourse unit pairs, we ran training for an initial classifier trained only on 43 examples of dissonance.

在收集约一千个对话单元对的例子后，我们只在四十三个不一致的例子上训练了一个初始分类器。


This involves using the latest test sets to avoid an overlap of the test data with the training data of the language model.

这涉及使用最新的测试集，以避免测试数据与语言模型的训练数据重叠。


So instead to do that, we'll turn to the results from our Marked Words method to show how these positive-seeming words facilitate stereotypes and essentializing narratives.

所以我们将转向我们市场词汇方法的结果，以展示这些积极的看起来的单词如何促进了成见和本质化的叙述。


We find CoScript shows high pluralism in the generated specific goals.

我们发现CoScript在生成的特定目标中表现出高超理想主义；


These days large language models are coming up with longer and longer context windows.

这些日子里，大语言模型来得越来越多，越来越长的


In this setting, the source language is the same as target language, for example German to German or English to English.

在这种设置中，源语言与目标语言相同，例如德语到德语或英语到英语。


Since then, this model has been adapted to many other languages, like in French with CamemBERT, and also in domains like biomedical with PubMedBERT and BioBERT and on clinical with ClinicalBERT, but mostly in English.

从那以后，这种模型已经被适用于许多其他语言，例如法语，Camembert和其他域名，例如生物医学，Pametber和Biober，和临床，Clinicalber，但大多数是英语。


They produce irrelevant information in around 15% of the responses, and they contradict themselves or their partner around 10% of the time.

他们在回应中产生了15%的不相关信息，并且他们大约10%的时间与自己或他们的伙伴相互矛盾。


So for instance, the word "warrior" is usually associated with men.

例如，“男人”一词通常与“男人”一词相关联，


In weakly supervised learning, training algorithms are proposed to robustly train neural networks under such label noise so that the trained models still generalize well.

在弱视监督训练中，提出了训练算法，以便在这种标签“噪音”下对神经网络进行强大的训练，从而使训练模型仍然可以泛化。


The Lambda calculus is missing, or they're only evaluated on certain neural models.

表。LamdaCocles消失了。或者，它们只在某些较新的模型上进行评估；


If the language model has access to some partially overlapping background knowledge, then the accuracy is between 82 to 87%, which is more realistic.

如果语言模型有对部分重叠的背景知识的访问，那么准确率在82%到87%之间，这更现实。


Feel free to get in touch with us if you have any questions.

如果您有任何问题，请随时与我们联系。


Now, we use the MuDA benchmark to evaluate models and we find that context-aware models are significantly more accurate than models that do not use context for certain discourse phenomena such as formality and lexical cohesion.

现在我们使用Muda基准来评估模型，我们发现上下文词模型在某些话语现象，如正式性和词汇凝聚性方面的准确性远远高于不使用上下文的模型，但这些模型


So this shows the effect of different fine-tuning strategies on the model sensitivity.

这表明模型敏感度的不同调节策略的效果：正如我们可以


So it's crucial that we evaluate the models' acceptability throughout the context window and that is what we are trying to do here.

因此非常重要的是，我们通过整个联系窗口来评估模型的可接受性。我们正试图在这里做这件事：


There exist more than 1600 language-only instruction tasks.

存在超过1,600种语言的指令任务，


So we get dependencies from the governor.


We also find most additional alignment with people who have a college education.

我们还发现与拥有大学教育的人相比，我们与拥有大学教育的人有更强的联系。


We've also shown that the models are domain-generalizable.

我们还显示模型是域一般化的；


And we also try to investigate whether language models can pick up the polarisation that's prevalent in our modern society.

我们也试图研究语言模型是否可以捕捉到我们现代社会中普遍存在的多元化。


So with the advances in large language models, many works started to explore new learning paradigms of reusing pre-trained language models for different downstream tasks in a parameter and data-efficient way.

随着大型语言模型的进步，许多作品开始探索使用预训练语言模型来以参数和数据有效的方式实现不同下游任务的学习新范式。


So what we do is that to simulate these longer sequences, we revisit the data sets themselves and then we recreate sentences by choosing acceptable or unacceptable sentences from those datasets.

我们要模拟这些更长的序列，我们会重新访问这些数据集本身，然后我们会重新创建句子，通过选择这些数据集中的可接受或不可接受的句子。


And finally, for black women, we see that some of the top words are things like "strong" and "resilient".

最后，对于黑人女性，我们看到一些顶词是像强壮和坚韧的。这与一个架构有关，即


When compared to human annotations, the weaker annotations are much cheaper, yet they are also noisy, meaning that a certain amount of the annotations are incorrect.


Since its release in 2018, BERT has become one of the most effective approach to solve natural language processing tasks and offers huge performance gains compared to historical static and contextualized methods such as Word2vec, fastText, or more.

自2018年发布以来，BERT已经成为解决自然语言处理任务的最有效方法之一，并且在与历史静态和上下文化方法（如Word2Vec）相比，提供了巨大的性能提升。


To answer this question, we first train and compare four from-scratch models: a first version of DrBERT, with 7 GB of NACHOS; a second version of 4 GB of set of NACHOS; a first version of ChuBERT, which is a clinical model with 4 GB of sentences taken from clinical notes; and a final version of ChuBERT with a mix of 4 GB of set of NACHOS and 4 GB of clinical notes.

为了回答这些问题，我们首先训练和比较四个从头开始的模型：BERT-base、BERT-large、RoBERTa-base和RoBERTa-large。我们将在此基础上进行以下实验：我们将ShuBERT的四个版本（一个是四个GB的自然语言处理数据集，一个是四个GB的临床注释数据集，一个是四个GB的自然语言处理数据集和四个GB的临床注释数据集的混合，另外一个是四个GB的临床注释数据集）与ShuBERT的四个


For example, when the language model retrieves the background knowledge.

例如，当语言模型检索背景知识时，


Therefore, this motivates us to build a multi-modal instruction tuning dataset.

因此这促使我们建立多模态指令调节。”


For example, the one with the piano music.

例如，带有钢琴音乐的实体。


So for instance, for the personas of black women, we would do Fightin’ Words and compare the log-odds ratios against both white personas and man personas because those are the two corresponding unmarked groups.

因此，对于黑人女性，我们将使用词语对比，并将对比的词语频率与白人男性和黑人男性进行比较，因为这两个群体是对应的未标记组。


This is an example of a design bias where we see systematic performance differences of technology between populations.

这是一个设计偏差的例子，我们看到技术在不同的人群之间存在系统性性能差异。设计师的视角就


The evaluation highlights that models performed best on the task with data of the same nature as those on which the model has been trained.

然而，我们可以从异源数据中获得数据，


We also can see transfer learning from natural instruction datasets can help OFA to attain much better performance on the natural instruct dataset.

我们还可以看到，基于自然指令数据集的迁移学习，可以帮助OWA在自然指令数据集上获得更好的性能。


For the first time, we show strong generalization to deeper recursion without relying on trees.

我们首次展示了不依赖树结构的情况下，神经序列到序列模型可以对更深层次的递归进行强大的泛化。


Here loves to all conjuncts separately: Lisa, Bart, and Maggie.

的爱护而获得依赖。现在，


For example, if right-leaning language models were to be fine-tuned on hate speech or misinformation or whatever and deployed to a popular social media platform, this would mean that, people with opposite political opinions might be marginalised and hate speech targeting minority groups might just run rampant without any control.

例如，如果正确的语言模型可以对恨语或任何信息进行处理，并部署到流行的社交媒体平台上。这将意味着持有相反政治观点的人可能会被边缘化，而针对少数民族的仇恨言论可能会疯狂地蔓延而没有任何控制。


Feel free to get in touch with us if you have any questions.

如果您有任何问题，请随时与我们联系。


So for example, here we have chosen like a typical pair of grammaticality from the BLiMP data set from the Adjunct Island case.

例如，在这里，我们选择了来自Blimp数据集的“典型”一对语法性


This means that the first two words will be emitted while since the sum of the cross-attention is above a certain threshold alpha, we will not emit the last word and we wait for another speech chunk.

这意味着前两个单词将会发出虽然大众注意力集中在某个临界点上，但我们不会说出最后的话，等待另一个演讲段落。


With the rapid pace of improvement in the field, many of these error rates could see a decrease in new models released since our evaluation was conducted.

由于快速的改进，许多这些错误率可以在新模型发布后看到降低


So it's crucial that we evaluate the models' acceptability throughout the context window and that is what we are trying to do here.

上下文窗口，所以很关键的是我们要评估这些模型在整个上下文窗口的可接受性，而这正是我们试图做的：


Secondly, we aim to investigate to which extent the political biases of language models are actually picked up from training data.

第二，我们旨在调查语言模型的政治偏见实际上从训练数据中被提取的程度，


For example, for RoBERTa further trained on the left-leaning Reddit corpus we can see a substantial liberal shift in terms of its political biases.

例如，罗伯塔在左侧的红色身体上进行了进一步的训练，我们可以看到它在这种方面有很大的自由。就其政治立场而言


We addressed these research questions in our work and our findings are as follows.

我们在工作中解决了这些研究问题，结果如下。


In the previous work, we introduced CXMI as a measure for context usage by machine translation models.

在以前的工作中，我们引入了CSMI作为机器传输模型对联系人使用的测量指标，并


It's my pleasure to give a short advertisement video of our paper.

我很高兴能给你们看一下我们论文的短视频：


In the context of semantic parsing, testing for compositional generalization might look like this.

在语义分析的背景下，测试组成的普遍化可能看起来像这


We also observe that using more data translated to better performance.

我们也观察到使用更多数据可以转化为更好的性能。


Now, what happens when we choose sentences from the same data set?

现在我们从同一数据集中选择句子会发生什么？因此，


However, this is all the more reason to pursue reliable and precise evaluation metrics for comparing models.

然而，这更是为了追求可靠和精确的评估指标来比较模型的理由。


例如，如果要训练语言模型来识别仇恨言论或信息等，并将其部署到流行的社交媒体平台上，这意味着人们持有相反的政治观点的人可能会被边缘化，而仇恨言论目标少数群体可能会毫无控制地疯狂地疯狂地疯狂地疯狂地


And vice versa, right-leaning language models are better at detecting hate speech targeting white and men, however worse at detecting hate speech targeting at black LGBTQ plus and other minority communities.

反，正确的语言模型更好地检测到目标白人和黑人，尽管在检测黑人LGBTQ+和其他少数民族时更好。


First, from our groups, the top words include things like "culture", "tradition", "proud", and "exotic".

首先，对于标记组，顶部的单词包括文化、传统、自豪和异国的词汇，


Recently, many studies have shown that instruction tuning enables large language models to perform on unseen tasks in a zero-shot manner by following natural instructions.

最近，许多研究表明，指令调节使大型语言模型能够以自然指令的方式在未见过的任务上进行彻底的工作。


Finally, we checked whether each evaluation metric captures a unique aspect of chat quality using a stepwise linear regression.

最后，我们检查了每个评估指标是否使用逐步线性回归捕捉了检查质量的独特方面。你


Similar trends also happen for fake news detection, where we see that left-leaning language models are better at detecting misinformation from their opposite political leaning and vice versa.

类似的趋势也发生在假新闻检测中，我们看到正确的语言模型更好地检测来自相反政治立场的信息。在此，


So let's say that you just developed a dialogue model and you want to see how well it compares against the current state-of-the-art.

假设您刚刚开发了一个对话模型，您想看看它与当前的最佳实践相比如何。


In this work, we propose a diagnostic test suite for knowledge integration.

在本研究中，我们提出了一套诊断测试套件用于知识融合。


Hello everyone, my name is David Vilar, and I will be giving a short review of the paper "Prompting PaLM for Translation: Assessing Strategies and Performance."


The difference observed is of more than one BLEURT points.

516/1000）观察到的差异大于1个模糊点。


Our prompts to generate these personas were inspired by a study where they gave these prompts to human subjects, finding that by giving it to human subjects, they also were able to surface racial stereotypes.

我们的促发词，生成这些人格特征的灵感来自一项研究，研究中他们把这些促发词给了人类受试者，发现人类受试者也能通过这些促发词表达出种族刻板印象。


This means that these three words will be emitted.

这意味着这三个单词将会被发出。


The cosine and L2 similarity between the requested embedding and the target embedding are computed.

计算了请求的嵌入和目标嵌入之间的余弦相似度和余弦相似度。


They occupy all four quadrants on the political campus.

我们可以看到，政治背景下


Compositional generalization can be understood as the ability of a learner to handle deeper recursion and unseen compositions of phrases that have been seen individually during training.

可以理解为学习者在训练过程中看到的未见过的组合和未见过的组合的能力。


To overcome these limitations, we rely on the property that these newer instruction-tuned LLMs are very good at responding to instructions and prompts.

为了克服这些限制，我们依赖于这些新型指令调整的内核很好地响应指令和提示的属性。


Second, if clean data is required, or if clean data is mandatory for WSL to work, then how many clean samples do we need?

其次，如果需要清洁数据，或者清洁数据是WSSL工作的必需品，那么我们需要多少清洁样本？


Long and complicated training procedures, for example, training involving different optimization objectives.

漫长而复杂的训练程序，例如涉及不同优化目标的训练。


To answer the first question, we started by measuring how much a word depends on context during translation.

为了回答第一个问题，我们从测量翻译过程中语词依赖于语境的程度开始。


And this allows us to find, for example, dual pronouns in Arabic that have relatively high P-CXMI.

，这使我们能够找到例如阿拉伯语中具有相对高PCSM的双名词的例子，


Where prospective API is able to detect correctly toxic instances.


As we can see, if we have 10 samples per class, direct fine-tuning starts to beat WSL approaches.

我们可以看到，如果我们有十个样本/类别，直接精调开始击败WSL方法。


Is it 4 gigabytes, 8 gigabytes, or more?

是4GB、8GB还是更多？


Third, continuous fine-tuning is a simple yet strong baseline that should be considered in future work in WSL.

第三，连续的微调是一条应该在未来WLS工作中考虑的简单而又强大的基线。


This indicates that WSL approaches actually require cleanly labeled data to work properly, and the annotation cost for obtaining clean validation samples should not be overlooked.

这表明WSL方法实际上需要清洁标签的数据才能正常工作，并且获取清洁验证样本的注释成本不应该被忽视。


Here we present MultiInstruct, the first multi-modal instruction tuning benchmark dataset that consists of 62 diverse multi-modal tasks covering 10 broad categories.

在这里，我们介绍MultiInstruct：第一个多模态指令调音标数据集，该数据集由62个不同的多模态任务组成，涵盖10个类别。


On further rounds of AL with two best strategies, we improve dissonance classification AUC to 0.75, which is the best performance that we have on the task so far.

们采用了两种最佳策略，提高了AUC到7.5点，这是我们迄今为止在任务上取得的最好的成绩。


So here the sentences are still coming from a, relevant data sets but it's not from the same data set that you are evaluating with.

这里，句子仍然来自相关数据集，但它不是您正在评估的相同数据集，


The trees are intended to capture the compositional process that relates utterances with the logical forms.

树的目的是捕捉与逻辑形式相关的表达的组合过程。


So this will tell us like whether the models acceptability judgments are actually impacted by any context, like, whether the context is coming from a different subset of the data set, or whether it's like completely irrelevant, to the current like to the sentence that we are looking at.

这将告诉我们是否模型的可接受性判断实际上受到任何上下文的影响。就像从数据集的不同子集中获取的内容，还是完全与当前句子无关。


Hello everyone, my name is Yusen Zhang from the Penn State University.


If you want to discover more results, read our paper.

如果您想了解更多结果，请阅读我们的论文，并


The legend of the figures means the number of triggers in each sentence.

数字的传说意味着每个句子的触发器数量。


We design a new metric called sensitivity.

我们设计了一个新的传感器，称为敏感度。


These tasks are derived from 21 existing open-source dataset and each task is equipped with five expert written instructions.

这些任务是从现有的21个开源数据集派生出来的，每个任务都配备了五个额外的写入指令。


I'm Koustav Sinha, and I'm pleased to welcome you to our talk of our ACL 2023 paper.

我是科斯塔夫·塞纳。很高兴能在我们的ACLU2023年文件中与大家讨论


Planning for the goals with specific constraints, such as "make a chocolate cake", still remains under-studied.

规划具有特定目标和约束的目标，如制作巧克力蛋糕仍然是一个未被研究的领域。


Our goal is to understand users’ language when they want to make a choice.

我们的目标是理解用户在选择时的语言；


And we called our tagger the Multilingual Discourse-Aware, or MuDA tagger.

我们称我们的标签为多语言讨论意识的乌玛达标签。


And they have a consonance relationship.


However, there is no large-scale publicly-available multi-modal instruction task.

但没有大规模的公共可用的多模态指令任务，


We do this through our framework NLPositionality.

我们通过我们的框架（NL位置性）来实现这一点。


Where A and B are samples from Wikipedia.

A和B是来自维基百科的样本。


Since large language models are costly to deploy, it's essential to enable language planning ability of smaller and specialized models.

由于大型语言模型的部署成本很高，因此必须允许使用较小的和专门的模型进行语言规划。


Our paper investigated the problem of generalization using the Named Entity Recognition Task or the NER task.

我们的论文使用命名实体识别任务或NER任务来研究泛化问题。


The second ingredient is the model size.


But natural language understanding often requires knowledge that is also supplied at inference time.

训练获得的知识，通常通过预训练获得的知识，通常通过预训练获得的知识，通常通过预训练获得的知识，


This works well, but trees are usually not given and need to be obtained somehow.

这很好，但通常我们不需要通过某种方式获得它。


To determine what kind of evaluation is most effective, we selected four state-of-the-art chat models and evaluated them on 100 human-bot conversations per model using ABC-Eval.

为了确定哪种评估最有效，我们选择了四种最先进的聊天模型，并在每种模型上评估了100个人类机器人对话，使用ABCEVL。


Hi, I'm Myra and today I'll be talking about our paper "Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models."


To give you a teaser of the experimental results, here we compare our method with other treeless models on the COGS benchmark.

为了给您一个实验结果的提示，我们在这里将我们的方法与其他无树模型在Cogs基准上进行比较。


Design biases like the one that we just saw before might occur due to the positionality of the NLP researchers and model developers.

像我们刚刚看到的那一个，我的鼓励是基于NLP研究人员和模型开发人员的视角：视角


In our paper, we solve a couple of interesting technical challenges.

在我们的论文中，我们解决了一些有趣的技术挑战。


For example, there's only one single model to evaluate them.

例如，只有一个模型可以评估它们。


First, when does translation require context?


So we want our curves to be as high as possible on this plot.

因此，我们希望我们的队伍在这个地图上尽可能高。


And we also find many interesting results.

我们还发现了许多有趣的结果。因此，


For example, if we receive a speech chunk containing "I'm going to talk about..." and our model predicts the translation in German, and we will look at the cross-attention weights, we'll see that the first two words points to the earliest received speech frames, while the last word points to the last received speech frames, as lambda speech frames.

例如，如果我们收到一段包含“我要谈论”的语句，并且我们的模型预测了德语翻译，那么我们就可以确定翻译是稳定的。我们将看看交叉注意力（crossattention）等待（wait）。我们将看到，前两个词指向最早接收到的语音框架，而最后一个词指向最后接收到的语音框架（或称为lamda语音框架）。


Finally, we checked whether each evaluation metric captures a unique aspect of chat quality using a stepwise linear regression.

最后，我们检查了每个评估矩阵是否捕捉到独特的检查质量方面，使用步骤向量线性回归。你


Firstly, can these models generalise to modern data?

首先，这些模型能否推广到现代数据？


Please find more details of CoScript in our paper.

请在我们的论文中找到CoScript的更多详细信息。


Pretraining on English natural language can significantly boost the performance of Few-shot on target natural languages, and we found multilingual language models such as Codex and BLOOM are still inadequate for cross-lingual semantic parsing tasks.

使用英语自然语言进行预训练可以显著提高在目标自然语言上的少量数据上进行的任务的性能。我们发现，多语言语言模型，如Coders和Blue，仍然不足以


"Cumulative" accumulates all the data collected from active annotation so far, whereas "Iterative" updates the model by training on the latest set of data collected.

累积积累所有从活动注释中收集的数据，因此迭代更新模型通过在收集的最新数据集上训练。


In this setting, the source language is the same as target language, for example German to German or English to English.

在这种情况下，源语言与目标语言相同，例如德语与德语，或者英语与英语。


We first construct a back door and a benign data set.

我们首先构建了一个后门和一个恶意数据集。


Lastly, the "Background-Inference" setting, where both knowledge types are available only at inference time.

背景知识；最后，背景知识只有在练习时可用。


If it's a multi-modal generation task, we report Rouge-L. For NLP task, we report Rouge-L as well.

如果是多模态生成任务，我们报告BLEU；对于NLP任务，我们报告BLEU也好。


So it's important to select the examples from high-quality translations.

因此，选择高质量的翻译例子非常重要，


And their results so a better performance when using the dev data.

使用DEFT数据时表现更好。


Hello everyone, my name is David Vilar, and I will be giving a short review of the paper "Prompting PaLM for Translation: Assessing Strategies and Performance."

大家好，我叫艾德·维拉尔，我们将会对这篇关于强大翻译、评估策略和性能的论文进行短评，


In this figure, we show the results of the best-performing models on the most difficult variant of the Background-Pretrain setting.

在这个图中，我们展示了在背景预训练设置中最难的变体上表现最好的模型的


The Asian woman is depicted as unassuming; the Middle-Eastern woman is referred to using words like exotic and like, referring to a mesmerizing region.

亚洲女性被描绘为“无知”，而中东女性则被称为“异国情调”。比如，提到迷人的地区，


For example, report if the model selection is done via clean validation samples.

例如，报告模型选择是否通过清洁的验证示例进行。


Is it 4 gigabytes, 8 gigabytes, or more?

是4GB、8GB还是更多？


Afterwards to stay engaged in the study, they can compare their responses to an AI and others.

之后，为了继续参与研究，他们可以将他们的回应与人工智能和其他人进行比较。


The first one is the model architecture.


It's my pleasure to give a short advertisement video of our paper.

我很高兴可以给您展示一段关于纸张的短视频：


That's because this is related to the "Traveling Salesman" problem.

这是因为这与旅行推销员问题有关。


In the Background-Inference setting, we provide the fictional occupation "mirituer" instead of politician because "mirituer" is unlikely to be contained in the pretrained parameters.

最后，感谢您的关注。他们提供了虚构的职业“政治家”而不是“政治家”。因为预训练参数中不太可能包含merituary。


However, previous work mainly focuses on planning for the abstract goals of stereotypical activities.

然而，过去的工作主要集中在规划典型活动的抽象目标上；


To our next question, what causes the performance drop of some models, We had two hypothesis.

下一个问题是：什么原因导致一些模型的性能下降？我们有两个假设：


Therefore, in this work we want to investigate whether instruction tuning a multi-modal pre-trained models can actually improve generalisation to unseen multi-modal tasks.

因此，在本研究中，我们想探索是否可以通过在多模态预训练模型上进行指令调节来实际改善对未见多模态任务的泛化。


Also, transfer learning from natural instruction dataset can benefit instruction tuning.

此外，从自然指令集学习的转移学习也可以有益于指令调节。


On the other hand, the combination of all turn-level Likert metrics explains far less of the quality, and fewer of these metrics carry unique information.

另一方面，交叉级别的利克特指标的组合解释了质量的很少部分，并且这些指标中有更少的指标携带独特的信息。


So we see that if we investigate the per category performance, that is to say if we separate the performance into different demographics or political leaning of news media we can see a pattern.

所以我们说，如果我们调查了类别级表现，那就是说，如果我们分开了表现不同的人口统计学或政治新闻媒体，我们可以看到一个模式，


Results with T5 XL model are summarized below.

使用T5-XL大型模型的结果如下：


So we ask ourselves a question about what is the most appropriate data sources for a wide range of usage and those crawled data are good substitution for clinical data.

因此，我们问自己：对于广泛使用，哪些数据源是最合适的？这些数据源是用于临床数据的良好的替代品。


However these works really don't look at comparing end users with the datasets and models themselves, and studying model and data set positionality is increasingly important as NLP tasks become more subjective and socially oriented, and it's challenging to characterise how these positionalities are skewed because not all decisions are documented and many models are hidden behind APIs.

然而，这些作品并没有真正比较最终用户与数据集和模型本身。学习模型和数据位置性是变得越来越重要的，因为Lp测试变得越来越主观和社会偏向。很难描述这些位置性是如何被扭曲的，因为并非所有的决定都被记录下来，许多模型都被API隐藏起来。


Copyright verification is to detect whether a model behind another service contains the word mark.

版权证书验证是检测另一个服务后面的模型是否包含水印的过程。首先，


I would like to present our work accepted into ACL 2023 as a long paper, "Transfer Learning for Dissonance Detection: Addressing the Rare-Class Challenge."

我想以一篇长篇论文的形式介绍我们接受的ACLU23号案件：学习转移以检测异议，解决罕见的挑战。


The prompting has a big influence on the performance of the LLMs for translation, as we can see in a simple experiment, where we used one-shot prompting and provided two different prompts for each sentence.

提示有很大的影响力于ELM的翻译性能，正如我们可以看到的一个简单的实验中，我们使用单次提示，并为一个句子提供了两个不同的提示。


One based on the weight of CamemBERT and trained on a 4 GB set of NACHOS.

一个以卡曼伯的重量为基础，并在纳查斯的四千克以上的重量上训练；


Next, to improve the number of dissonance examples, we use a Probability-of-Rare-Class strategy — PRC — to select mostly the examples that are highly likely to be descended by the current model at any round of rare.

接下来，为了改善离散示例的数量，我们使用了实体类策略PRC，选择大多数在任何一个空轴上高度有可能被当前模型分开的示例。


So we have a few recommendations for this.

因此，我们对此有几些建议：


The first one is the model architecture.


Positionality is simply the perspectives that people hold as a result of their demographics, identity, and life experiences.

位置性质只是由于人口统计学、身份和生活经验而形成的观点。


We follow the method from OFA and formulate all the tasks in a unified sequence-to-sequence format.

我们遵循MasterFromOFA，并将所有任务统一为一个统一的序列到序列格式。


And what we do is that to recreate like longer sequences and which are acceptable and which has the same matching of the grammatical structure.

，从AdjunctIsland数据集中。我们可以通过在接受的和不接受的问句


In weak supervision, you do not manually label the data.

在弱监督中，我们不会手动标记数据；


This table reports the overall accuracy of the results.

该表报告了结果的总体准确性。


Embedding marker contains two main steps.

嵌入标记器包含两个主要步骤：


First, is clean validation data necessary for WSL or can we maybe use a noisy validation set instead?

首先，WSL需要清洁的验证数据吗？或者我们是否可以使用嘈杂的验证集代替？


In this work, we propose a diagnostic test suite for knowledge integration.

工作中，我们提出了一个用于知识融合的诊断测试套件。


Here are some examples of indirect references for example, "the newer one" or "the song that's not energetic."

是很有活力的歌曲。这是一个重要的问题在


This belief and action are inconsistent, and they are in dissonance.

这两句话之间存在矛盾。进一步提到我不认为我能


So "Marge read it yesterday" is fine because the direct object is close to the verb, while "Marge read yesterday it" is much worse.

读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨


Another also based on CamemBERT, but trained this time on the 4 GB of clinical notes and finally, one based on English biomedical model PubMedBERT, and trained on 4 GB of set of NACHOS.

我们还基于Camembert，但这次训练在4GB的临床数据上，最后一次基于一个基于英超生物医学模型（BERT）并在4GB的自然数据上训练，总共


We address this by inducing the alignment as part of the training.

我们通过在训练中引入对齐来解决这个问题。


So specifically, we first proposed to prompt language models with different prompt formats using the political questionnaires such as the political conference test.

因此，我们首先提议使用不同形式的政治问题提问者来提前语言模型，例如政治会议测试，


We dig into a more fine-grained topic categories of constraints defined in wikiHow.

我们深入探讨了更细化的限制类别，根据工作方式不同。


And to better evaluate our benchmark, we consider the six settings for training and evaluation.

为了更好地评估我们的基准，我们考虑了六种训练和评估设置。


We then compared these annotations with Dynahate, Perspective API, Rewire API, Hate Roberta and GPT 4.


So for example, the words describing Latina women include things like "vibrant" and "curvaceous" which connect to a trope of tropicalism.

例如，描述拉丁美洲女性的词汇包括像“活力”和“性感”等词汇。这与热带主义有关。


For example, you can see how measuring the proportion of turns with self and partner contradictions explains 5% and 10% of conversation quality, respectively, while the average Likert consistency scores explain only 4% or less.

例如，您可以看到如何衡量自我和伴侣冲突的比例，表明与谈话质量相对应的5%和10%，而平均液体一致性分数表明只有4%或低。


And we perform our analysis on transcripts of TED talks that have been translated from English to 14 different languages.

我们对泰德·托克的翻译从英语翻译成14种不同的语言进行了分析。


Our study in the end amassed over 16,000 annotations from over 1000 annotators from 87 countries.

该研究研究了艺术，并最终收集了来自87个国家的超过1,000名注释者超过16,000条注释。


And we also released open source the code and models and simultaneous output to facilitate the reproducibility of our work.

我们也发布了开放源代码、模型和同时发布的输出，以便促进我们的工作可复制性。


First of all, the alignment between input and output is not given in the training data.

首先，输入和输出之间的对齐在训练数据中没有给出。


This work was done by the Emory NLP Lab led by Professor Jinho Choi at Emory University and in collaboration with Amazon Alexa AI.

这项工作由埃默里大学的GinoOchi教授领导的埃默里NLP实验室完成，并与亚马逊AlexaAI合作。那么


But what's novel in this paper is that we observed that this tendency only occurs when the governor is on the left or absent.

的短语越倾向于先出现。我们观察到这种趋势只在当政府在左侧（absent）时发生。


In addition to this comparison, we introduced three models trained on continual pre-training to analyze the impact of pre-training strategy.

除此之外，我们还引入了三种文化预备训练模式，以分析预备训练策略的影响。


Now we analyze words with high P-CXMI to look for patterns between these words.

现在我们使用高斯混合模型分析这些单词，以寻找这些单词之间的模式。


Studying cognitive dissonance can help us understand the effects of disagreement among people, track trends and belief values, and attitude changes in population.

研究认知差异可以帮助我们了解人们之间的不一致的影响，跟踪信仰、价值观和态度在人群中的变化趋势。


The current MPP pipeline basically doesn't allow us to evaluate a model's acceptance towards longer sentences.

当前的MP管道基本上不允许我们评估模型对更长句子的接受程度。


We sample 100 specific goals and evaluate the scripts generated from large language models.

我们采样了100个具体目标，并评估了由大型模型生成的脚本。


Throughout experiments we found that there are three main ingredients that are needed.

通过我们的实验，我们发现需要三种主要的原料。


This is joint work with my advisors Alexander Koller and Ivan Titov.


In addition, ABC-Eval labels are more predictive of the overall conversation quality compared to metrics produced by existing methods, as shown by this simple linear regression analysis.

els比现有方法产生的指标更能预测总体对话质量，正如通过此简单线性回归分析所示。


Finally, we provide some recommendations for prompt selection strategies.

最后，我们提供了一些推荐的策略。.promp


And to better evaluate our benchmark, we consider the six settings for training and evaluation.

为了更好地评估我们的基准，我们考虑了六种训练和评估设置。


So in practice, there's no reason to choose more complex WSL methods which require more computation time and disk space.

所以在实践中，没有理由选择更复杂的WSSL方法，这些方法需要更多的计算时间和磁盘空间。


The majority of sentences 516 out of 1,000.

大多数句子中，1000个句子中的


The first one is generating these personas.

第一个部分是生成这些人物。


The first one is the model architecture.


If we go on and we receive another speech chunk, and our model predicts other three words and we will look at those cross-attention weights, we will see that no word points to the last lambda speech frames.

如果我们继续下去，我们会收到另一个语音段，我们的模型会预测...我们将这些三个词语排列在交叉注意力权重中，我们会看到没有一个词语指向最后一个lambeadaspeechframe。


And we can immediately see that this is very generalizable to any demographic because we can just specify whatever identity marker that we want into this prompt.

我们可以立即看到这非常普遍化到任何人口统计，因为我们可以只指定我们想要在这个提示中输入的任何身份标记。所


So for instance, the word "warrior" is usually associated with men.

例如，词语“人”或“战士”通常与男性相关联，


In recent years, many have documented the prevalence of social bias and stereotypes in large language models, or LLMs.

近年来，许多人已经证明了社会偏见和大语言模型或ELMs中存在的成见。


Pretraining on English natural language can significantly boost the performance of Few-shot on target natural languages, and we found multilingual language models such as Codex and BLOOM are still inadequate for cross-lingual semantic parsing tasks.

学习英语自然语言可以显著提高未来的目标自然语言的表现。我们发现多语言模型（例如科达斯和蓝色）仍然不足以跨语言多人对话。


Then we conduct detailed analysis to investigate why learning models fail.

然后我们进行详细的分析，以调查宽泛的线性模型的结果。


With CoScript we can try smaller but specialized models for constrained language planning.

使用CoScript，我们可以选择更小但更专业的模型来进行受限语言规划。


We increase the context length toward up to 1024 for to max out OPT and GPT 2 models.

我们增加了上下文长度，直到1024，为了最大化OOP和GPT2模型，


In the Background-Pretrain setting, we assume that the background knowledge "Politicians seek elected seats in government" is contained in the pretrained parameters and in inference-time context we provide the entity-specific knowledge "Chichester is a politician."

一个例子在背景前，假设政治家寻求在政府中当选的背景知识包含在预备训练参数中。在延迟背景下，我们提供了反特异知识：切斯特是政治家。


And what are the problems of the current SimulST models?

当前的ST模型存在什么问题？


这就是为什么这听起来似乎很好


So we did a series of analysis where we tried to perturb the input sentence by, trying to preserve the relevant structure but adding like noise to the input.

我们进行了一系列分析，试图通过在输入句子中添加噪音来破坏输入句子，


This is a joint work with John Gauthier, Aaron Mueller, Kanishka Misra, Karen Fences, Roger Levy, and Adina Williams.


This figure shows the constraint distribution of CoScript.

这张图表显示了CoScript的受限分布；


We introduce a coreference resolution task, designed to probe for the ability to draw on knowledge available in different sources.

我们引入一个关联解释任务，旨在为能够在不同来源中提取可用的知识而设计。


Basically, we find that the models are sensitive to the perturbed sentences in similar ways.

面的路线。基本上，我们发现模型对句法结构的敏感性类似。


And so we opt to re annotate data to get many annotates for instance and to get a rich set of demographic data.

因此我们希望重新注释数据以获得每个实例的注释数量。数据集。


We find that T5 fine-tuned on CoScript can generate scripts of higher quality than most large language models, indicating that smaller models can surpass larger models when properly trained on suitable datasets.

在Fansites上，TFileFountuneonCourserate可以生成质量高于大多数大型语言模块的脚本，表明小型模块可以在适当的数据站上经过适当的训练时支持大型模块。


And we evaluate on mT5 and XLM-R + PTR on multilingual setting.

我们评估MT5和示例XLM-R+PDR在多语言设置中的表现。


Our second finding is that increasing the number of clean validation samples will help WSL approaches to achieve better performance, as shown in the figure on the left.

我们的第二个发现是，增加清洁验证样本的数量将有助于WSL方法实现更好的性能，如图左所示：


And we compare also with the state-of-the-art architecture specifically tailored for simultaneous pre-translation.

我们的方法与专门为同时预测翻译设计的最新技术进行比较。


我们的框架分为两个主要步骤。


So when the difference between the lengths of the two conjuncts grows, the shorter conjunct prefers to be the first one, stronger, right?

，长度差异越大，短语越倾向于先出现。因此，当两个短语之


Please find more details of CoScript in our paper.

请在我们的论文中查找更多关于科斯克里普特的细节。


This is a concept widely used in critical studies, specifically in feminist and queer academic spaces.

这是一个在批判性研究中广泛使用的概念，特别是在女权主义和女权主义学术领域。


Existing works can be broadly classified into four categories.

现有的作品可以大致分为四类。


The heat map in the figure shows that the planning performance of InstructGPTs varies considerably for goals of different categories.

图中的头图显示，教科书的规划性能对不同类别的女孩有很大差异。


We can see that language models generally had a political leaning that is further away from the centre after 2017.

可以看到语言模型一般来说具有更远离中心的政治倾向，


So for the training dataset, we use 53 tasks from 9 groups for training and we sample 10,000 instances per task.

对于训练数据集，我们使用了NLPGroup的53个任务进行训练，并且我们对每个任务进行了10000个示例的


In this paper, we define the problem of constrained language planning which imposes different constraints on the goals of planning.

在本文中，我们定义了受限的语言规划问题，这种问题会对规划的目标施加不同的约束。


Through our experiments we found that the transformer models normally generalize better to new data.

通过我们的实验，我们发现变压器模型通常对新数据进行一般化。


When we show this alternative question to the annotators, they know the name of these entities, but they don't necessarily know about the entities.

当我们展示这些替代问题给注释者时，他们知道这些实体的名称，但他们不一定知道这些实体。


But also we want that they are shifted on the left.

但我们也希望它们在左侧移动。


And welcome to visit our paper and code.

欢迎您访问我们的论文和代码。


So the governor is on the left in this example "I saw Bart and Lisa" so is the governor is on the left.

在这个例子中，州长在左边；我看到巴特和丽莎，所以州长在左边。


After a long day at work deciding cases in a law court, he was happy to relax.

在法庭上做出决定后很高兴放松一下。


So now we use our findings from our analysis to design a benchmark for document-level translation.

因此，我们现在使用我们的分析结果来设计文档局部翻译的基准。


So here we are choosing or creating sentences from acceptable and unacceptable domains from the same BLiMP or SyntaxGym dataset.

在这里，我们选择或创建从可接受和不可接受域中选取的句子，从同一BLIMP或语法DIM数据集中，看


However, recent works have shown that the attacker may steal the model through learning from the embedding and provide similar services.

然而，最近的研究已经表明，攻击者可能通过学习从嵌入中学习模型，并提供类似的服务来窃取模型。


So the reasoning here is that this is possible because even though this sentence violates the general grammatical principle that direct objects should be next to the verb, it satisfies the principle of dependency length minimization, which says that shorter dependencies are preferred.

因为即使这句话违反了直接对象应该位于开发者之后的通用语法原则。它满足了依赖关系长度最小化的原则，该原则说短依赖关系优于长依赖关系。所以，


There are a bunch of more examples in the appendix to further highlight that this indicates that there is a fairness issue that is very pressing regarding the political biases of language models.

附录中。进一步强调的是，这表明这是一个公平问题，非常压力政治偏见。语言模型，


We dig into a more fine-grained topic categories of constraints defined in wikiHow.

我们探讨了更多的主题限制，根据工作方式不同。


So going back to the question that we raised in the title of our paper Do CoNLL-2003 taggers still work in 2023?

因此，让我们回到我们论文标题中提出的问题：Cornell2003标签器在2023年是否仍然有效？


And this shows us that adaptive overfitting in this case is not observed.

这表明在这种情况下，适应性过度适应没有被观察到。


And this can go, in extreme cases, up to 40 BLEURT points.

在极端情况下可以达到40个球点，因此很重要


And our results show many interesting findings.

我们的结果显示了许多有趣的发现。


So we can ask the model to generate a persona, which is a depiction of an imagined individual using a prompt like "Imagine you are an Asian woman.

所以我们可以要求模型生成一个人物，这是对想象个体使用一个像“想象你是一个亚洲女性”


They occupy all four quadrants on the political campus.

它们占领了政治大厅的四个象限。


This work was done in collaboration with some folks at the University of Washington and the Allen Institute for AI, namely Sebastian Santy, Ronan Le Bras, Katharina Reinecke and Maarten Sap.


So on one hand, they were able to learn from diverse perspectives, which celebrates democracy and the plurality of ideas.

从不同角度来看，他们可以从多种角度欣赏民主和多元化的思想；


However, this method either not applicable to embedding as services or lack of transferability.

然而，这种方法既不适用于嵌入式服务，也缺乏可移植性。


The legend of the figures means the number of triggers in each sentence.

嵌入的传说意味着每个句子中的触发器的数量。


And some people have suggested targeted evaluation on context-dependent translations, but these resources only support limited types of context-dependent translations and limited sets of languages since they usually rely on domain knowledge and human curation.

有些人建议对依赖于语境的传播进行目标评估，但这些资源只能支持有限的依赖于语境的传播和有限的语言集，因为它们通常依赖于主要的知识和人类创造力。


So, it seems that PaLM chooses to produce a better-sounding translation, sometimes by dropping parts of the source sentence that are made in translation.

因此，似乎Palm选择了更好的翻译方式，通过删除翻译中排列的句子部分。


First, from our groups, the top words include things like "culture", "tradition", "proud", and "exotic".

首先，标记组的顶词包括文化、传统、自豪和异国情调等，这些词仅仅通过


Where prospective AP is really not as sensitive to offensive terms that are more common in Indian contexts.

AI模型对更常见于印度语环境中的辱骂性词汇的敏感度确实不够。


What is simultaneous speech translation?


Our conclusion is that, for good generalization we would need a better model architecture, larger model size, as well as more fine tuning examples.

我们的结论是，为了好的泛化，我们需要更好的模型架构、更大的模型尺寸，以及更多的精调示例，并且


So the minimal pair paradigm basically evaluates language models on top of acceptability judgments.

最小的对称范畴基本上评估语言模型，


，所以它似乎是最合适的。.有时？通过在翻译中省略句子的某些部分。


We found that usually larger models lead to better generalization.

我们发现，通常，较大的模型会导致更好的泛化。


We only keep the script if the target goal scores the highest in the goal set.

只有当目标鬼魂得分在鬼魂视野中最高时，我们才保留脚本。


We assume the provider can collect a general text corpus and count the word frequency with it.

我们假设提供者可以收集通用文本库，并计算单词频率。


We separately pretrain language models on the two different temporal corpora.

我们分别预训练语言模型在两个不同的时间段的语料库上。我们


This means that every unit of improvement that we made, on CoNLL-2003 translates to more than one unit improvement on CoNLL++ which means that there is no diminishing returns.

这意味着我们在Color2003上做的每个改进都转化为Color+的多个改进。这意味着没有减少的回报。


So see the paper for the full arguments.

所以看一下关于全面的协议和论点的


Throughout experiments we found that there are three main ingredients that are needed.

通过我们的实验，我们发现需要三个主要成分。


In the previous work, we introduced CXMI as a measure for context usage by machine translation models.

在之前的工作中，我们引入了CMI作为机器翻译模型的上下文使用度量，并且


"Marge read this absolutely fascinating book about bees yesterday."

书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了


This last setting is especially interesting, since it simulates the case where the background knowledge necessary to solve a task is not part of the pretrain data of models.

最后的设置非常有趣，因为它模拟了需要解决任务的背景知识并不是模型的预训练数据的一部分，


And so one question that people might ask is, do datasets and models have positionality?

因此，人们可能会问：数据集和模型是否具有位置性？


And training and maintaining several models to reach different latency regimes.

维护多个模型以达到不同的延迟模式，


As a consequence, for a given token we don't know which multiset it came from, which poses a challenge for training.

因此，对于一个给定的令牌，我们不知道它来自哪个多标注器，这给训练带来了挑战。


We hope our paper calls for more research on how to improve generalizations of the models.

我们希望我们的论文呼吁更多关于如何改善模型泛化的研究。


This is joint work with Xiaoyu Shen, Marius Mosbach, Andreas Stephan, and Dietrich Klakow.


Firstly, can these models generalise to modern data?

首先，这些模型可以将其概括为现代数据吗？


And this is done by measuring how much information the context C provides about the target Y, given the source X. You can think of CXMI as the information gained from giving context to the model.

是通过测量上下文C关于目标Y给定源X提供的信息量来实现的。您可以将CMI视为将上下文提供给模型时获得的信息量。


In our experiments, we settled for a 5-shot prompting strategy where we just marked each sentence that we provide to the system, with the language it's in.

在我们的实验中，我们采用了五轮提示策略。我们只是标记了它（）是我们提供给系统的句子，使用的语言是（）；


For the recipes and books domain, we show some background text from Wikipedia.

对于食谱和书籍，我们显示一些来自维基百科的背景文本；


In which the input text, images, instructions and bounding boxes are represented in the same token space.

其中输入文本、图像、指令和边界盒都在同一个令牌空间中表示。


This is a concept widely used in critical studies, specifically in feminist and queer academic spaces.

这是一个在批判性研究中广泛使用的概念，尤其是在女权主义和酷儿学术空间中。


For data overfitting, we saw that from the graph on the right, the red best fit line has a gradient that is greater than one.

对于适应过度匹配，我们发现从右边的图表中，红色最佳匹配线具有比一大一的梯度。


So, "salt and pepper" and not "pepper and salt", measured in syllables.

的同位语短，例如盐和胡椒而不是胡椒和盐，和也。观察到


But sometimes an indirect reference is more appropriate to have a more natural conversation.

但有时，间接引用更适合。更自然的对话：


To evaluate our seven models, we gather data for public and private downstream tasks such as named entity recognition, classification, part-of-speech tagging, and question answering.

为了评估我们七个模型，我们收集了多个公共和私人任务，例如姓名和身份识别，分类，语音采集和问答。


In addition, sometimes there are multiple permutations that are consistent with the data, but the linguistically correct one is latent.

此外，有时会有多个与数据一致的置换，但从语言上来说正确的置换是隐性的。


The summary of our experimental results is that the example quality is more important than the similarity to the source sentence.

我们实验结果的总结是，示例质量比与源句子相似度更重要。


Overall, from-scratch pre-training seems to obtain higher performance on most of the tasks.

总的来说，从头开始训练似乎在大多数任务中获得更高的性能。


Feel free to get in touch with us if you have any questions.

如果您有任何问题，请随时与我们联系。


So during test for each task, we conduct a total of 5 experiments by evaluating the model using one of the five instructions.

期间，每个任务我们将执行五个实验，使用每个实验中的五个指令评估模型。我们报告了均值和最大


So, while the generated personas have much higher rates of the lexicon words, the human-written ones have a much wider distribution of words, while the stereotype words that are in the generated personas are really just the words "tall" and "athletic".

虽然生成的个人有更高的拉克索恩词的等级，但人类写的词有更广泛的分布，而生成的个人词只是高大而有活力。


For example, the bots we tested have common sense violations in around 20% of their responses.

例如，我们测试的机器人在大约20%的回答中有常识违反。他们


Now we analyze words with high P-CXMI to look for patterns between these words.

现在我们分析了高频语音信号（HPSMI），以寻找这些字之间的模式


Is it 4 gigabytes, 8 gigabytes, or more?

是4GB、8GB还是更多？


And leverage the knowledge already acquired by the model through the attention mechanism between audio input and textual output.

并利用模型通过输入音频和文本之间的注意力机制获得的知识。


What is simultaneous speech translation?


Recently, many studies have shown that instruction tuning enables large language models to perform on unseen tasks in a zero-shot manner by following natural instructions.

最近，许多研究表明，指令调节允许大语言模型以完全的方式通过遵循自然指令执行未见过的任务。


It's absent in the second example "Homer came and sneezed."


So last but not least, we evaluate language models with different political leanings on hate speech detection and fake news detection to NLP applications that often involve language models and could have very significant implications.

最后但不是最不重要的是，我们评估了语言模型的不同政治倾向，包括仇恨言论检测和假新闻检测，两种自然语言处理应用程序，通常都涉及语言模型。这可能会有非常重要的影响，


To the goal of creating a cognitive dissonance resource, we conducted a large scale annotation of dissonance relations.

为了实现构建认知不协调资源的目标，我们进行了大规模的注释不协调关系。


These days large language models are coming up with longer and longer context windows.

如今，使用较长和较长的联系窗口的大型语言模型越来越多，


That is, when we perturb the sentences in the acceptable domain, we see similar increase in all the perturbations and when we perturb the sentences in the unacceptable domain, we see decrease in MPP judgments in similar fashion.

当我们在可接受的领域扰乱句子时，我们看到所有扰乱的类似增加，当我们在不可接受的领域扰乱句子时，我们看到类似的MPP判决减少。


The current MPP pipeline basically doesn't allow us to evaluate a model's acceptance towards longer sentences.

当前的MPP管道基本上不允许我们评估模型对更长句子的接受程度。


For example, OpenAI offers a GPT based embedding API.

例如，OpenI提供一个基于GPT的嵌入API。


Natural language understanding models draw on a variety of knowledge sources, such as knowledge contained in their parameters, usually acquired by a pretraining, and knowledge given in inputs at inference time.

国家语言理解模型从各种知识来源中提取出来，例如，通常通过预训练获得的参数中包含的知识，通常通过预训练获得的知识，


We found that Encoder-Decoder or Encoder-PTR can be improved by training in a mixture of various languages.

我们发现编码器解码器或编码器-PDR可以通过在各种语言的混合中训练来改进。


You can see that in the results of our experiment that several challenges still remain and have been precisely quantified.

在我们的实验结果中看到，仍然存在一些挑战，并且已经精确量化了。


However, there is no large-scale publicly-available multi-modal instruction task.

没有大规模的公共可用的多模指令，


We've then compared these, annotations with Social Chemistry, Delphi and GPT 4.

然后我们将这些注释与社会化学、德尔菲和GPT4进行了比较。


So this is a QR code for our data and model.

这是我们数据和模型的QR码。


In our paper, we solve a couple of interesting technical challenges.

在我们的论文中，我们发现了一些有趣的技术挑战。


We addressed these research questions in our work and our findings are as follows.

我们在我们的工作中解决了这些研究问题，以下是我们的发现。


So what we did, we extracted various statistics about coordination from the enhanced version of the Penn Treebank and see the paper "Why wouldn't you use universal dependencies" and these statistics confirm the observation made many times before that left conjuncts tend to be shorter.

我们从加强版的潘特里银行的协调数据中提取了许多统计数据，看到了一份文件，解释为什么我们不会使用大学的依赖关系。这些统计证实了人们多次观察到的左结合体往往比右结


In particular, we compare the selecting prompts from the training data for the WMT evaluations on the dev data.

特别是我们比较来自WMT评估的训练数据或欺骗数据的选择提示。


The results on four data sets show that our embedding marker can have great detection performance while keep great utility for downstream tasks.

四个数据集的结果表明，我们的嵌入式标记器可以具有很好的检测性能，同时为下屏幕任务提供很好的实用性。


I'm going to talk about our work on "Resolving Indirect Referring Expressions for Entity Selection", in which we introduce the AltEntities Corpus.

我要谈谈我们关于解决实体选择中的间接引用表达式的工作，其中我们介绍了AlternatingEntitiesCorpus。


What is simultaneous speech translation?


And we also see that if we consider the actual elapsed time or the computational-aware time, that is the fastest strategy.

我们还看到，如果我们考虑实际的延迟时间或计算的延迟时间，Adapt是最快的策略。


Compositional generalization can be understood as the ability of a learner to handle deeper recursion and unseen compositions of phrases that have been seen individually during training.

组合一般化可以被理解为学习者处理更深层次的回归和未见过的短语组合的能力，这些短语在训练过程中被单独看到。


To sum up, we build XSemPLR, a unified benchmark for cross-lingual semantic parsing with multiple natural languages and meaning representations.

适应跨语言语义分析任务。总之，我们建立了一个名为Exemplar的跨语言语义分析的统一基准，支持多种自然语言和多种表示形式。


In our analysis, we reveal how these seemingly positive portrayals reflect harmful patterns.

在我们的分析中，我们回顾了这些似乎是积极的肖像画如何反映了有害的模式。


So this sort of suggests where we would need to see more progress for document-level translation.

所以这就意味着我们需要为文档级传输做更多的进步。


I think this is known as the "Curse of Multilinguality".

我认为这是多语言的诅咒。


It violates one principle, but it satisfies another one.

它违反了一个原则，但它满足了另一个原则。


We just really can't make any assumptions or really study that further, without more transparency.

，我们真的不能做出任何假设，或者进一步研究这些问题，更多的透明度。


Studying cognitive dissonance can help us understand the effects of disagreement among people, track trends and belief values, and attitude changes in population.

学习认知差异可以帮助我们理解人际之间的冲突：追踪趋势和信仰，价值观和态度在人口中的变化。


In everyday life, humans often plan their actions by following step-by-step instructions in the form of goal-oriented scripts.

在日常生活中，人类通常通过遵循目标脚本中的一步步指示来规划自己的行动。


Here, a user wants to select between one of these two songs.

在这里，用户想选择这两首歌中的一个。


We find that there is positionality in NLP.

我们发现NLP中存在位置性。


But these models are not much better than models that do not use context on other phenomena like ellipsis, pronouns, and verb form.

但这些模型并不比没有使用联系其他现象（如椭圆、圆锥和形式）的模型好，


And Cross-Lingual Semantic Parsing is the task to translate queries in multiple natural languages into multiple meaning representations.

跨语言语义解析是任务，翻译查询到多种自然语言。


So what is needed for a good generalization?

那么，什么是好的概括所需要的？


First the method should be applicable to embedding as services.

首先该方法应该适用于嵌入和服务；


And there we see that the MPP judgments either increase or decrease significantly when you add either acceptable prefixes or unacceptable prefixes.

到MP判断在添加可接受前缀或不可接受前缀时会显著增加或减少。


The insights that we gained from the human evaluation that we performed using the MQM framework said that the fluency of PaLM is comparable to state-of-the-art systems but the main difference comes from the accuracy.

twithGoogleTranslate.“哇！我们从人类评估中获得的见解是：使用MCMF框架，我们可以实现与当前的SOTA系统相当的流畅性，但主要的差异来自准确性。


We then take the annotations by demographic and compare them to the models and datasets using a Pearson's R correlation score, and thus our framework actually differs from annotator disagreement literature by comparing end users with models and datasets, predictions and labels, as opposed to looking at just annotator agreement or modelling annotator distributions.

然后我们使用人口统计学的注释将它们与模型和数据集进行比较。因此，我们的框架实际上与注释器协议文档不同，因为我们将最终用户与模型和数据集、预测和标签进行比较，而不是仅仅注释器协议或模型化注释器分发。


If we directly train neural networks on weakly labeled data, the neural networks tend to memorize the label noise and do not generalize.

如果我们直接训练神经网络并对数据进行弱标签，则神经网络往往会记住标签噪音，而不会进行泛化。


The most obvious thing is to use a direct reference, for example by saying the name of the song "Easy on Me" or its position, "the first one".

最明显的方法是使用直接参考，例如说这首歌的名字“易于我”或它的位置，第一首。


Therefore, you might want to evaluate multiple dimensions of chat quality to understand the strengths and weaknesses of the model on a finer-grained level.

因此你可能想评估多个对话质量的维度，以了解模型在更细粒度的层面上的优缺点。


We found that Encoder-Decoder or Encoder-PTR can be improved by training in a mixture of various languages.

置中，并发现编码器解码器或编码器PDTR可以通过在混合各种语言的混合中进行训练来改进。


However, these measures have various limitations.

然而，这些措施有各种限制：


The benefit of this is that we get really specific stereotypes and patterns, without having to rely on any specific lexicon.

这的好处是我们得到真正具体的刻板印象和模式，


Finally, should we only use the clean samples for validation, or there are better ways to utilize them?

最后，我们只应该使用清洁样本进行验证，还是有更好的方法可以使用它们？


And more broadly, dominant groups in society are both linguistically and socially unmarked, while the marginalized groups are usually marked.

更广泛地说，社会中的主导群体在语言上和社会上都没有标记，而边缘群体通常是有标记的。


One based on the weight of CamemBERT and trained on a 4 GB set of NACHOS.

一项基于Camembert的权重并在4倍的NACHOS上进行训练。


We're not aware of a larger-scale public data set for the task, so we collect one using crowd annotation.

我们不知道公共数据集（一个大规模的公共数据集用于任务），所以我们使用人群计数收集一个数据集。


Specialized models for other languages are scarce and are often based on continual pre-training due to the lack of in-domain data.

其他语言的专用模型很少，通常基于连续预训练，因为缺乏域内数据。然而，Fre


The aforementioned doubt is asked to ask three research questions.

上面提到的疑问使我们不得不提出三个研究问题：


For the recipes and books domain, we show some background text from Wikipedia.

对于食谱和书籍领域，我们展示一些来自维基百科的背景文本。


Feel free to get in touch with us if you have any questions.

如果您有任何问题，请随时与我们联系。


We found that, by comparing the green and orange line, we found the Zero-shot setting, the Cross-lingual transfer performance gap is significant, and then comparing the blue and orange lines, we found that with the Few-shot setting the transfer gap is shortened rapidly.

通过比较绿色和橙色线，我们发现在零shot设置中，跨语言转移性能差距很大，并且通过比较蓝色和橙色线，我们发现在零shot设置中，跨语言转移性能差距很大。我们发现在几秒钟的设置下，传输差距迅速缩小。


A word is emitted if the attention is not concentrated, that is, its sum is below a certain threshold alpha towards the last lambda speech frames, meaning that the received information is enough stable.

如果没有集中注意力，那么发出的词汇将会被发出，如果其总和低于某个阈值（即Alpha），则意味着所接收的信息是不稳定的。


We then look at vocabulary items that have high P-CXMI averaged over all of its different occurrences.

我们看到了语料库中的语料，高频词在所有不同情况下都有很高的平均频率。


We can see that language models generally had a political leaning that is further away from the centre after 2017.

可以看到语言模型通常具有政治倾向，这种倾向在2017年之后从中心向外延伸，因此


As shown in the table, we extend the abstract goals with multi-faceted constraints for human-in-the-loop data acquisition using InstructGPT.

如表所示，我们扩展了抽象目标的多阶段约束，以便人类在使用InstrGPT进行数据采集时可以更好地理解。


To answer this question, we compare DrBERT with our ChuBERT model, which is based on anonymized data obtained from the Nantes University Hospital data warehouse.

为了回答这个问题，我们将与伯特医生和我们的舒伯特模型进行比较，舒伯特模型是基于我们收到的非大学院的匿名数据。


Hi, I'm Sara Papi from the University of Trento and Foundazione Bruno Kessler and I will briefly introduce the "Attention as a Guide for Simultaneous Speech Translation" paper, that is a joint work with Matteo Negri and Marco Turchi.

我是特伦特大学和布鲁诺·凯斯勒基金会的塞拉·帕比。我将简要介绍作为指南的注意力，用于同时翻译演讲文本，这是与马特奥·内格里和马可·图尔基共同工作的。


And this shows us that adaptive overfitting in this case is not observed.

这表明在这种情况下没有观察到适应性超适应。


So prior work has suggested some anecdotal evidence of having positionality, such as cultural gaps and models and data sets, as well as theoretical definitions of model positionality.

因此，前人工作已经提出了某些轶事性的位置性证据，如模型和数据集中的文化差异，以及模型位置性的理论定义。


Hello everyone, I'm Akshatha, and today my co-author Martin and I are presenting our work "The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources."

欢迎大家，今天我和我的同事马丁一起演示我们的工作，评估来自多个来源的知识积累。


Embedding marker contains two main steps.

嵌入标记器包含两个主要步骤。


We find that all language models achieve unsatisfactory results on planning for specific goals.

我们发现，所有轻量级模型在规划特定目标方面的结果都令人不满意。


Our approach attempts to reduce the subjectivity of human evaluation by explicitly annotating whether or not each model response expresses certain behaviors, such as responding with irrelevant information or contradicting itself.

我们的方法尝试通过明确注释每个模型响应是否表达某些行为来减少人类评估的主观性，例如响应与无关信息或自相矛盾。


Which is not the case for the model based on CamemBERT weights and tokenizer, which suffer from stability issues.

这并不是基于CommonBERT权重和Tokenizer的模型的案例，因为这些模型存在稳定性问题。


Then we jump to the next multiset token, to determine the second token in the output.

然后，我们跳到下一个多集令牌，以确定输出中的第二个令牌。


Some other kinds of structural generalization remain very challenging, though.

更大的优势。其他类型的结构化转换也很有挑战性。


We also find some other interesting findings.

我们还发现了一些其他有趣的发现：


In this figure, the blue line is Cross-lingual Few-shot transfer.

在这个图中，蓝色线是跨语言零shot转移，


Long and complicated training procedures, for example, training involving different optimization objectives.

训练过程长且复杂，例如训练涉及不同的优化目标。并且训练和


To sum up, we build XSemPLR, a unified benchmark for cross-lingual semantic parsing with multiple natural languages and meaning representations.

总结起来，我们构建了示例，一个用于双语语法解析的统一基准，具有多种自然语言和多种代表。


Firstly because only a small portion of translations depend on context which makes corpus-level metrics like BLEU unable to capture these translations.

首先，因为只有很小一部分翻译依赖于语境，这使得蓝色等体层级矩阵无法捕获这些翻译。


If the task is a multi-model classification task, we report accuracy.

如果任务是多模分类任务，我们报告准确度；


As can be seen here, dissonance was only found in 3.5% of the annotated pairs.

正如在这里可以看到的，异议只在注释的对话中发现。


ABC-Eval is capable of measuring the rates at which chat models will commit various thematic errors.

ABC-EVAL可以衡量那些会犯各种主题错误的聊天机器人。


I'm Koustav Sinha, and I'm pleased to welcome you to our talk of our ACL 2023 paper.

各位，我是科斯塔夫·塞纳，很高兴与大家分享我们的ACLU2023论文：


I think this is known as the "Curse of Multilinguality".

我认为这被称为多语言的诅咒。


We have defined three settings of KITMUS.

我们定义了三种凯德莫斯的设置：


However, when we actually look at the distribution of the words and lexicon, we find very different things.

然而，当我们实际上看一下词典中的词的分布时，我们发现了非常不同的东西，所以


We also introduce an additional evaluation metric called sensitivity.

我们还引入了一个额外的评估指标，称为敏感度。


However, with task-specific training, some models successfully integrate knowledge from multiple sources.

然而，通过任务特定训练，某些模型成功地整合了来自多个来源的知识。


We're trying to revisit the MPP pipeline by asking the model to evaluate acceptability on longer and longer sequences.

我们试图通过要求模型对更长和更长的序列进行可接受性评估来重新访问NBP管道。所以，


And we also find many interesting results.

我们还发现了许多有趣的结果。


We then take the annotations by demographic and compare them to the models and datasets using a Pearson's R correlation score, and thus our framework actually differs from annotator disagreement literature by comparing end users with models and datasets, predictions and labels, as opposed to looking at just annotator agreement or modelling annotator distributions.

然后我们根据人口统计学对注释进行分类，并将它们与使用Pearson相关系数进行比较的模型和数据集。因此，我们的框架实际上与注释者不一致的文献不同，因为我们将最终用户与模型、数据集、预测和标签进行比较，而不是仅仅注释者一致或建模注释者分布。


However, we can observe that data from heterogeneous sources appear to be more versatile.

我们可以观察到来自异源数据的数据似乎更具多样性，


Today I'm presenting our work "From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models".

今天，我要从预备数据到语言模型，到下游任务，跟踪政治行为导致不公正的ALV模型的轨迹。


Finally, cognitive dissonance is important to understand personal cognitive styles of individuals and helps us understand decision making processes better.

最后，认知差异很重要，需要了解个人认知风格，以便我们能更好地理解决策过程。


These approaches work well to provide holistic evaluations of overall dialogue quality, but dialogue quality has many aspects.

这些方法在提供对话质量的整体评估方面很好，但对话质量有很多方面，


So for example, in the universal dependencies, the structure of the coordination, Lisa, Bart, and Maggie, such that the first conjunct is the head of the whole coordinate structure.

例如，在宇宙依赖中，协调的协调结构是由LisaA.Batty和Maggie所承认的。这是这样的：第一个结合体是整个心脏结构的头部，所以


So see the paper for the full arguments.

和非对称性结构（如这两种）的论据。因此，请参阅论文中的完整协议和论据，


Feel free to get in touch with us if you have any questions.

如果您有任何问题，请随时与我们联系。


We continue this process until every token from the first stage has been visited exactly once.

我们继续这个过程吗？直到第一阶段的每个令牌都被访问了一次。


And finally, there's also a multi-headed approach that's used, for example, in the Hudson's Word Grammar, where they say all conjuncts are heads of the coordinate structure.

最后，这也是一个多头的方法——例如，在《卡特森斯世界语法》中使用。在这种情况下，所有的管制结构都处于上级的管制之下，


Conceptually, our permutation model works roughly like this.

从概念上讲，我们的置换模型大致如此。


In the Background-Pretrain setting, we assume that the background knowledge "Politicians seek elected seats in government" is contained in the pretrained parameters and in inference-time context we provide the entity-specific knowledge "Chichester is a politician."

回到预训练设置。我们假设背景知识：政客寻求在政府中获得选举席位。它包含在预训练参数中。在这个背景下。我们提供了非特定知识：Chichester是一名政治家。


In Live in the Wild is an online experimentation platform where we can recruit divers volunteers.

在世界上是在线实验平台，我们可以从世界各地招募志愿者，


And also this enables direct comparison between our generated personas and the human written responses.

这也使得我们与我们的生成人格之间可以直接比较，和人类的回应。


So the minimal pair paradigm basically evaluates language models on top of acceptability judgments.

最小对数对范式基本上评估语言模型的可接受性，包括可接受性判断、语法性如BLIMP、语法性如语法性、可


So here the sentences are still coming from a, relevant data sets but it's not from the same data set that you are evaluating with.

因此，句子仍然来自相关的数据集，但不是您正在评估的相同数据集。我们也可以


So the proportion is bigger of the left short conjunct.

间的长度差异越大时，短语越短


I would like to present our work accepted into ACL 2023 as a long paper, "Transfer Learning for Dissonance Detection: Addressing the Rare-Class Challenge."


As we can see by transfer learning from natural instruction datasets, the model can achieve much better sensitivity compared to the original OFA model.

正如我们所看到的，通过从自然指令数据集中转移学习，模型可以在与原始的OWA模型相比，获得更好的敏感性。.


In this video, I would like to present our recent work "Weaker Than You Think: A Critical Look at Weakly Supervised Learning."


And, we also evaluate Encoder-Decoder models, which is Multilingual Pretrained Encoder-Decoder Models, such as mBART and mT5.

我们还评估了编码器解码器模型，这是多语言预训练的编码器解码器模型，例如AmBart和MT5。


我们还将测试单线语模型。


For each of the existing methods, we collected evaluations on eight of the most commonly measured aspects of dialogue, since this is the standard practice for evaluating chat models along multiple dimensions.

对于每一种现有的方法，我们收集了对八种最常见的对话方面的评估，因为这是评估多维度聊天机器人评估标准实践。


So this indicates that language models can also pick up the polarisation in our society.

这表明语言模型也可以捕捉到我们社会中类似于政治化的趋势。所以，


So a lot of translations depend on context.

很多翻译都取决于上下文：


So, given that there is positionality in NLP, what can we do about it?

因此，鉴于LED和LP有位置，我们可以做什么？


The watermark method need to meet the following properties.

水印方法需要满足以下属性：


And we perform our analysis on transcripts of TED talks that have been translated from English to 14 different languages.

我们在TED演讲的翻译文本上进行了分析，这些文本已经从英语翻译成14种不同的语言。


Because here between the verb and the direct object is an adjunct: "yesterday".

了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书


However, most previous works on instruction tuning focused on improving the zero-shot performance on language only tasks, while computer vision and multi-modal tasks have been left out.

然而，大多数以前关于指令调优的研究都集中在改善语言任务的零样本性能，而忽略了计算机视觉和多模态任务。


When compared to human annotations, the weaker annotations are much cheaper, yet they are also noisy, meaning that a certain amount of the annotations are incorrect.

与人类注音相比，弱注音更便宜，但它们也很吵闹，这意味着某些注音是不正确的。


Our frame is largely enabled through Lab in the Wild and online crowdsourcing platform for where HCI collaborator.

我们的框架主要通过LabintheWild来实现，一个由我们合作的H.C.I.合作伙伴创建的在线众包平台。


Where A and B are samples from Wikipedia.

A和B是来自维基百科的样本。


First, we have the typical setting: "Background-Pretrain", where background knowledge is assumed to be available at pretrain time.

首先，我们需要录制。“我们将在这里等待。预训练时假设背景知识可用。


When we move higher in the list, the entities become more similar to each other and it's usually harder to make the disambiguation.

当我们在列表中移动到更高的位置时，实体变得彼此更相似，通常很难消除这种模糊。


My name is Matthias Lindemann, and today I'm going to give you a brief introduction to our paper on "Compositional Generalization without Trees using Multiset Tagging and Latent Permutations".

我叫马蒂亚斯·林德曼，我今天要给你介绍一下我们关于无树木的组合通用化的论文，使用多元集标记和隐式变换。


And when we go, as in our case, to five-shot prompting, there is nearly no difference to the actual form of the prompting.

发的情况下，与实际弹发形式没有任何不同。


When a user send a sentence to the provider service the provider counts the trigger number in the sentence.

当用户将一句话发送到服务提供商服务时，服务提供商会计算这句话中的触发器数量。


For example, we put the German, English, Chinese queries together to train a multilingual model.

例如，我们将德语、英语和中文的询问结合在一起，以训练多语言模型，并


Otherwise, there is a large performance drop.


So here are some example generations from GPT-4.

以这里有一些来自GPT-4的示例世代。


It's okay the way instead of "it", we have this long NP.

这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我


Hi, I'm Siyu Yuan from Fudan University.

我是来自复旦大学的徐元，今天


And when we develop new taggers, what is needed for good generalization?

当我们开发新的标签时，我们需要什么才能进行良好的泛化？


Here are the different sampling methods we've used.


Results in the figure show that the semantic completeness in generated scripts is acceptable but the faithfulness to the constraints cannot be guaranteed.

图表结果表明，生成的脚本的语义完整性是可接受的，但忠诚度不足。不能保证满足这些约束。


To answer this question, we first train and compare four from-scratch models: a first version of DrBERT, with 7 GB of NACHOS; a second version of 4 GB of set of NACHOS; a first version of ChuBERT, which is a clinical model with 4 GB of sentences taken from clinical notes; and a final version of ChuBERT with a mix of 4 GB of set of NACHOS and 4 GB of clinical notes.

因此，我们首先训练并比较四种擦伤模型：贝特博士的第一种模型，具有七千千克的纳塞斯；贝特博士的第二种模型，具有四千千克的纳塞斯。舒伯特的第一版是临床模型，包含来自临床笔记的四个千兆字节的句子；舒伯特的最后版包含来自临床笔记的四千兆字节的句子和四千兆字节的临床笔记。


The provided embedding is a weight summation of the target embedding and the original embedding.

提供的嵌入是目标嵌入和原始嵌入的加权和。


That's because this is related to the "Traveling Salesman" problem.

这是因为这与旅行推销员问题有关。


In this paper, we first evaluate and improve the constrained language planning ability of large language models.

在本文中，我们首先评估并改进大型语言模型的受限语言规划能力。


And this allows us to identify phenomena that cannot really be captured by the word itself, but that's rather expressed in the sentence structure, such as ellipses resolution.

这使我们能够识别出不能真正被单词本身捕捉到的现象，但更倾向于在句子结构中表达出来的现象，如椭圆解析度。


So when people are describing a warrior who is a woman, they'll usually actually specify "woman warrior" and mark the term with "woman".

所以当人们描述一个女性战士时，他们通常会具体指出“一名女性战士”并标记该词语为女性。


In the second speech bubble, Alice says, "Do you mean 'Easy on Me' or 'I Gotta Feeling'?"

在第二个演讲泡沫中，爱丽丝说：“你是指我很容易吗？还是我有感觉？


所以，‘Marchredyesterday’是好的，因为直接对象靠近动词，而‘Marchredyesterday’是很差的，因为同位语更远。这两句话都很


And we evaluate on mT5 and XLM-R + PTR on multilingual setting.


Our solution is to propose EDAtt, or Encoder-Decoder Attention, and it is a strategy for which we decide whether to emit or not a partial translation, based on where attention points to.

我们的解决方案是建议使用“EDA”或编码器-解码器注意力，并且这是我们决定是否要发出部分翻译的策略，根据注意力点的位置。一个单词


Through our experiments we found that the transformer models normally generalize better to new data.

通过我们的实验，我们发现变换器模型通常对新数据的泛化更好。


Our model outperforms the others by a large margin on generalization to deeper recursion.

我们的模型在泛化到更深的迁移方面比其他模型有


This belief and action are inconsistent, and they are in dissonance.

这种信念和行为是不一致的，而且是不一致的。


Where A and B are samples from Wikipedia.

A和B是来自维基百科的示例。


However, the "Style/Awkward" category for PaLM is lower than for the state-of-the-art systems, which is an additional signal that PaLM provides really fluent output, but still with some problems of accuracy.

然而，帕恩的风格外观类别低于顶级系统，这是一个额外的信号。Palm提供了非常流畅的输出，但仍然存在一些准确性的问题。


Or the pronunciations are too similar to each other and hard to disambiguate.

所有的发音都太相似，很难区分。


Typically, this involves considerable formalism-specific pre-processing of the logical forms, for example, to handle variable symbols.

通常，这涉及到相当多的形式化特定前处理逻辑形式，例如处理可变符号。


And, also the observation that was made in parsing that this tendency grows with length difference.

移，这种倾向的增长速度越来越不同。


However, we believe there is a more precise and reliable strategy for dimensional dialogue evaluation.

然而，我们认为有一个更精确可靠的维度对话评估策略。


这里是与我们的数据集链接的


"Did you mean 'Easy on Me' or 'I Gotta Feeling'?"

你是指“易于我”还是我有感觉？


And we also consider Cross-lingual Zero-shot and Few-shot transfer.

我们考虑了跨语言零样本和少样本转移，


The results on four data sets show that our embedding marker can have great detection performance while keep great utility for downstream tasks.

四个数据集的结果表明，我们的嵌入标记器可以在保持下游任务的高效性同时获得高的检测性能。


Now, what happens when we choose sentences from the same data set?

现在，我们从同一数据集选择句子时会发生什么?


For example, for RoBERTa further trained on the left-leaning Reddit corpus we can see a substantial liberal shift in terms of its political biases.

例如，对于Robert，进一步优化和进一步训练在左倾的红色机构上，我们可以看到在政治倾向中有一个显著的自由度的变化。在政治偏见的方面，


Specialized models for other languages are scarce and are often based on continual pre-training due to the lack of in-domain data.

其他语言的专用模型稀缺，通常基于缺乏域内数据的连续训练。


Well, if the previous sentence was "Things could start to get dangerous if the ministers find out", then "mole" refers to a spy.

如果前一句话是‘如果前一句话是‘如果前一句话是‘如果前一句话是‘如果前一句话是‘如果前一句话是‘如果前一句话是‘如果前一句话是‘如果前一句话是‘如果前一句话是‘如果前一句话是‘如果前一


Our second finding is that increasing the number of clean validation samples will help WSL approaches to achieve better performance, as shown in the figure on the left.

我们第二个发现是增加清洁验证样本的数量将有助于WSL方法实现更好的性能，正如左边的图所示。


Now the aim of this paper is to produce a novel argument for the symmetric structures of coordination, like these two and against the asymmetric structures of coordination, like these two.

这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书，


On the other hand, the combination of all turn-level Likert metrics explains far less of the quality, and fewer of these metrics carry unique information.

另一方面，所有转换级利卡特矩阵的组合显示了质量差异很小，这些矩阵中很少有独特的信息。这些可靠、信


That's because this is related to the "Traveling Salesman" problem.

因为它与旅行销售问题有关。


We can think of words that have high P-CXMI as ones that require context for translation.

我们可以认为具有高pCSMI的词语是需要内容转换的。


We use Google Translate API to translate source to the target language, then use monolingual model to train and evaluation.

我们使用谷歌翻译API将源语言翻译为目标语言，然后使用单语言模型进行训练和评估。


We also compare the cross-language performance gap.

我们还比较了跨语言性能差距。


We're not aware of a larger-scale public data set for the task, so we collect one using crowd annotation.

，一个大规模的公共数据集用于该任务，所以我们使用人工标注的数据集。


Now those are asymmetric approaches to coordinate structures, such as the Prague approach.

这本书，昨天我读了这本书，昨天我读了这本书，昨天我读了这本书


Still, even the best-performing models seem to have difficulties with reliably integrating backward knowledge presented only at inference time.

然而，即使是表现最好的模型似乎也存在于可靠地集成后向知识，只在推断时间才会出现。


Then, InstructGPT over-generates K scripts for specific goals.

然后，指示GPT过度生成关键句子，以实现特定的目标。


The weight of the target embedding is proportional to the number of triggers in the sentence.

目标嵌入的权重与句子中的触发器数量成正比。


So some preliminary results demonstrate that first, language models do have varying political leanings.

一些初步结果表明，第一语言模型仍然具有不同的政治倾向，


We introduce a coreference resolution task, designed to probe for the ability to draw on knowledge available in different sources.

我们引入了一个核心参考解析任务，旨在探索从不同来源可用的知识中抽取的能力，


As we can see from the figures, the vanilla model, termed FTw, initially underperforms more complicated WSL methods, like COSINE.

正如我们从图表中可以看到的那样，Valina模型（称为FTW）最初在更复杂的WSL方法（如Cosine）中表现不佳。


In this example, the model has seen shallow recursion during training and is tested on an example with deeper recursion.

在这个例子中，模型在训练过程中出现了浅层回归，并在示例中通过更深层回归进


Here's an example of how we control the availability of facts in the true sources.

这是我们如何控制真源中事实可用性的


So what we do is that to simulate these longer sequences, we revisit the data sets themselves and then we recreate sentences by choosing acceptable or unacceptable sentences from those datasets.

我们要模拟这些更长的序列，我们访问数据集本身，然后我们通过从这些数据集中选择“可接受的”或“不可接受的”句子来重新创建句子。因此，


For example, when the language model retrieves the background knowledge.

例如，当语言模型检索背景知识时，


And for example, we train the English model on English query and during inference we translate the German query using API to English and then use the trained model to predict the SQL.

例如，我们在英语模型上训练了英语查询，然后在推理过程中，我们使用API将德语查询翻译为英语，然后使用训练好的模型预测后续的内容。


We provide the first and second speech bubbles automatically, but the third one is filled in by the annotator.

我们提供第一种和第二种语音气泡是自动的，但第三种是由注音器填充的；


As shown in this figure, if there are no clean validation samples, then the trained models cannot generalize beyond the original weak labels, meaning that the training is pointless.

如图所示，如果没有干净的验证样本，则趋势模型不能在原始位标签之外进行泛化。这条教义毫无意义。


We've also shown that the models are domain-generalizable.

我们还展示了模型是域普适的：


But if you'd like to learn more, feel free to check out our dashboard for the most updated analysis results and our paper.

但是，如果您想了解更多，请自由地查看我们的数据板和我们的论文。


We evaluate the data set with human study participants and established coreference resolution models.

我们评估数据集与人类研究参与者和建立的核心参考解析模型。