Additional experiments with fictional knowledge indicated even the best performing models, cannot reliably integrate backward knowledge provided only at inference time.

nicht nützlich. Weitere Experimente mit fiktivem Wissen zeigten, dass selbst die besten Modelle nicht funktionierten. Wir können die Hintergrundwissen, die nur zur Inferenzzeit bereitgestellt werden, nicht zuverlässig integrieren.


For example, Encoder-Decoder outperforms previous work or achieves comparable results.

fest: zum Beispiel, dass Encoder-Decoder über die Leistung von Prozessarbeiten hinausgeht oder vergleichbare Ergebnisse


Over the different strategies, we found that Cumulative performed equal or better than Iterative across the board.

verschiedenen Strategien finden wir heraus, dass die kumulative Form gleich oder besser als die iterative Form über den Tisch ist.


We further show many qualitative examples to see that language models with different political leanings do give different predictions to hate speech and misinformation examples based on their social categories.

viele qualitative Beispiele es gibt, um die Sprachmodelle mit unterschiedlichen politischen Bedeutungen zu sehen. Geben Sie verschiedene Prognosen, um Beispiele für Sprache und Informationen nach den sozialen Kategorien


For more details, please come to the full presentation of the paper.

für weitere Einzelheiten bitte die vollständige Präsentation des Papiers


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

Wir können sehen, dass die Sprachmodelle allgemein eine politische Ausrichtung haben, die sich weiter von der Mitte nach rechts


Our concrete recommendations for future work are as follows.

Unsere konkreten Empfehlungen für zukünftige Arbeit sind wie folgt.


And if we use word f-measure, then models with and without context have comparable performance.

verwenden, dann haben Modelle mit und ohne Kontext vergleichbare Leistungen.


So during training, we train it on English queries or the combination of English and German Few-shot queries to train a multilingual model to predict the SQL output.

Während des Trainings werde ich mich auf englische Anfragen oder die Kombination aus englischen und deutschen Fuzz-Quellen einstellen, um ein multilinguales Modell zu trainieren und den Output des Cecel zu vorhersagen.


OFA uses a unified vocabulary for language, image tokens and the coordinates of a bounding box.

OFA verwendet ein einheitliches Vokabular für Sprache, Bildsymbol und Koordinaten eines Bindungskastens.


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

Wie wir sehen können, zeigt dies die Auswirkungen der unterschiedlichen Abstimmungsstrategie auf die Modellempfindlichkeit. Durch das


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.

Das wird uns sagen, ob die Akzeptanzurteile des Modells tatsächlich von einem Kontext beeinflusst werden. Ob der Kontext aus einem anderen Unterdatensatz des Datensatzes stammt oder ob er zum aktuellen Satz, den wir betrachten, völlig irrelevant ist.


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

mein Modell zu kopieren und die Urheberrechte von Modellen für Verpackungen und Dienstleistungen in großen Sprachen


Therefore, in this paper we propose Embedding marker, which is a backdoor based watermark method applicable to embedding as services.

Deshalb schlagen wir in diesem Papier den Embedding-Marker vor, eine auf der Hintertür basierende Wasserzeichenmethode, die für Embedding-Dienste geeignet ist.


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

schließlich kommen wir zu den Experimenten. Wir geben Ihnen mehr Details darüber, wie man Zugriff auf diese Modelle erhält.


Firstly, can these models generalise to modern data?

Können diese Modelle auf moderne Daten generalisiert werden?


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

Spezialisierte Modelle für andere Sprachen sind selten und basieren oft auf kontinuierlicher Ausbildung aufgrund des Mangels an In-Domain-Daten. Allerdings hatte Frankreich bis jetzt kein


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

Dies ist ein QR-Code für unsere Daten und unser Modell.


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

sollten wir nur die sauberen Proben verwenden, oder gibt es bessere Möglichkeiten, sie zu nutzen?


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

in den Abbildungen zeigen, dass die semantische Vollständigkeit in den generierten Skripten akzeptabel ist, aber die Treue nicht. Diese Einschränkungen können nicht garantiert werden.


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.

Und wir können sofort sehen, dass dies für jede Demokratie sehr allgemein ist, weil wir einfach spezifizieren können, was immer Identitätsmarker wir in diesen Prompt wollen.


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

und diese Wörter definieren diese Gruppen nur durch ihre Beziehung zu ihrer Identität und unterscheiden sie als unterschiedlich von der weißen Norm.


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.

also werden wir stattdessen die Ergebnisse unserer Markierungswörter-Methode verwenden, um zu zeigen, wie diese positiv erscheinenden Wörter Stereotypen und Essenzialisierungen erleichtern.


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

Wir glauben jedoch, dass es sich um eine präzisere und zuverlässigere Strategie für die Auswertung von Dialogdimensionen handelt.


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

Zuerst verwenden Sie bereits vorhandene Offline-S-Modelle, ohne eine spezifische Architektur für SimulS zu trainieren oder anzupassen. Verwenden Sie


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.

Das Ziel des Papiers besteht nun darin, ein neues Argument für symmetrische Koordinationsstrukturen wie diese und gegen asymmetrische Koordinationsstrukturen wie diese zu liefern.


Daher wird uns gesagt, ob die Akzeptabilitätsurteile des Modells tatsächlich von irgendeinem Kontext beeinflusst werden. Wie, ob der Kontext aus einem anderen Teil des Datensatzes kommt oder ob er vollkommen irrelevant zum aktuellen Satz ist.


First, we look at part-of-speech tags that have high mean P-CXMI.

durch, zuerst werden die Sprechtags mit „hoch“ gekennzeichnet. Und das lässt


The first one is adaptive overfitting, which is overfitting costs by reusing the same test set over and over again and this is usually manifested as the diminishing returns on a new test set.

Die erste ist adaptiver Überanschnitt, der durch die wiederholte Verwendung desselben Testsets verursacht wird, und dies wird normalerweise als die Abnahme auf einem neuen Testset zurückkehrt.


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

Und eine Frage, die Leute stellen, ist, ob Datensätze von Modellen positionsspezifisch sind.


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

Sie nur ein Modell für jedes Latenzregime und behandeln Latenzien durch spezifische Parameter. Und


Previous work has exploited language models to plan for abstract goals of stereotypical activities such as "make a cake".

Vorherige Arbeiten haben Sprachmodelle genutzt, um Ziele abstrakter stereotypischer Aktivitäten wie Make-a-Kick zu planen,


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

ich bin Kostas Sana und freue mich, dass ich Sie zu unserer Diskussion über unser ACL 2023-Papier begrüßen darf: Sprachmodellakzeptabilitätsurteile


For recipes, we additionally show their images, again from Wikipedia, so that the annotators know how they look like.

Für die Rezepte zeigen wir zusätzlich ihre Bilder wieder von Wikipedia, damit die Kommentatoren wissen, wie sie aussehen.


einer Sprache ergibt, wie zwischen Sial und Kurdisch, aufdecken.


While the green line is the Monolingual Setting.

Einstellung ist. Wir stellten fest, dass bei


So, really just only the positive or at least non-negative ones.

Also wirklich nur die positiven oder zumindest nicht negativen.


So in this example here, where we perform translation from German into English, the German sentences, the source sentences, are marked with German colon and the English translations with English colon.

In diesem Beispiel, in dem wir von Deutsch ins Englische übersetzen, sind die deutschen Sätze mit deutscher Kursivschrift und die englischen Übersetzungen mit englischer Kursivschrift gekennzeichnet.


Because then it can be moved to the position after the adjunct.


So first, we look at the Wikipedia sentences, which are completely irrelevant to the current query pair, and there we find that the MPP judgments are mostly robust for arbitrary context length.

Zunächst betrachten wir die Satzformen von Wikipedia, die vollkommen irrelevant für die aktuelle Suchanfragepaar sind, und dort stellen wir fest, dass die MPD-Urteile für die willkürlichen Kontextlinien am stärksten sind.


Otherwise, there is a large performance drop.

ansonsten ist es ein großer Leistungsabfall,


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

Zweitens sollten Wsl-Ansätze mit zukünftigen Lernbaselines verglichen werden, die auf klaren Mustern basieren.


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

Unser Modell übertrifft die anderen bei der Generalisierung und der tieferen Rekursion deutlich.


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

Also wollen wir, dass unsere Kurse so hoch wie möglich sind


Each instance is randomly combined with one of its five instruction templates.

wobei jede Instanz zufällig mit einem der fünf Anweisungstemplates kombiniert


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

Also hier wählen wir Sätze aus akzeptablen und unakzeptablen Domänen aus dem gleichen Bimap- oder Syntax-Dataset


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.

Und das hilft dabei, Fälle wie diesen zu identifizieren, in denen Chinesisch eine Transliteration benötigt, um sicherzustellen, dass Sie die gleiche Transliteration im Dokument verwenden.


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

In der vorherigen Arbeit haben wir CxMI als Maß für die Kontextnutzung durch maschinelle Übersetzungsmodelle eingeführt


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

Im Gegensatz zur standardisierten Bewertung der Maschinen ist der Test nicht Teil der gleichen Verteilung, sondern enthält strukturelle logische Formen.


Instead, we label the data using weak labeling sources, such as simple heuristic rules, knowledge bases, or low-quality crowdsourcing, as illustrated in the figure on the right.

die Daten mithilfe schwacher Kennzeichnungsquellen, wie z. B. einfache heuristische Regeln, Wissensbasen oder qualitativ minderwertige Crowdsourcing, wie in der Abbildung rechts dargestellt.


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.

und dies ermöglicht es uns, Phänomene zu identifizieren, die nicht wirklich vom Wort selbst erfasst werden können, sondern vielmehr in einer Satzstruktur ausgedrückt werden, wie z. B. die Ellipsenauflösung. Daher verwenden


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

Für die Rezepte- und Bücher-Domain zeigen wir einige Hintergrundtexte von Wikipedia


Political news media are well covered in their pretraining data.

Politische Nachrichtenmedien werden in ihren Vorbereitungsdaten berücksichtigt, gemäß


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

Was passiert nun, wenn wir Sätze aus dem gleichen Datensatz auswählen?


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

und vergleichen auch mit der derzeitigen Architektur, die speziell für die simultane Übersetzung entwickelt wurde.


So if we do not sanitize political opinions in language model training data, the bias would propagate from pretraining data to language models to downstream tasks, ultimately creating fairness issues.

Wenn wir die politischen Meinungen in den Sprachmodellen nicht standardisieren, würden die Vorurteile von den Prä-Trainingsdaten auf die Sprachmodelle übertragen und schließlich Fairnessprobleme verursachen.


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

Diese Arbeit wurde vom Emory NLP-Lab, geleitet von Professor Gino Ochoa an der Emory University, und in Zusammenarbeit mit Amazon Alexa AI durchgeführt.


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

Zum Ziel, kognitive Dissonanzressourcen zu schaffen, haben wir eine große Anzahl von Dissonanzbeziehungen erstellt.


Conceptually, our permutation model works roughly like this.

Konzeptionell funktioniert unser Permutationsmodell in etwa wie dieses.


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

Wir hoffen, dass unser Papier zu weiteren Forschungen anregt, wie die Generalisierung der Modelle verbessert werden kann.


At the same time, we also found that the performance drop here is caused by temporal drift and kind of surprisingly, it is not caused by adaptive overfitting even though CoNLL-2003 has been used for over 20 years.

wir fest, dass der Leistungsabfall hier durch einen zeitlichen Drift verursacht wird und überraschenderweise nicht durch ein adaptives Überziehen verursacht wird, obwohl Cornal 2003 über 20 Jahre verwendet wurde.


And both of the women of color personas make references to ancestry while the white man persona has nothing of the sort.

und beide Frauen mit farbigen Persönlichkeiten beziehen sich auf Ahnen, während der weiße Mann keine solche Persönlichkeit hat.


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.

Missmatch-Szenario. Also die Sätze kommen immer noch aus relevanten Datensätzen, aber nicht aus demselben Datensatz, den Sie bewerten. Und wir


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

Während der Testphase führen wir für jede Aufgabe insgesamt fünf Experimente durch, indem wir das Modell mit einem der fünf Anweisungen


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

Wenn Sie Fragen haben, zögern Sie nicht, uns zu kontaktieren.


And with that, Bob sets the dialogue context.

und mit dem Lied setzt Bob den Dialogkontext.


For Asian women, the words are things like "petite" and "delicate" and "silky" which connects to a long history of Asian women being hyper-sexualized, seen as very docile and submissive, and so on.

sind für asiatische Frauen die Wörter Dinge wie „petite“ und „delicate“ und „silk“. Das verbindet sich mit einer langen Geschichte asiatischer Frauen, die hypersexualisiert sind, sehen als sehr dämonisch und unterwürfig und so


For example, OpenAI offers a GPT based embedding API.

Zum Beispiel bietet OpenI eine gpde-basierte Embedding-Applikation an.


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.

Allerdings untersuchen diese Arbeiten diese Arbeiten wirklich nicht, indem sie Endnutzer mit den Datensätzen und Modellen selbst vergleichen, und das Studium der Modell- und Datensatzposition ist zunehmend wichtig, da NLP-Aufgaben subjektiver und sozial orientierter werden, und es ist herausfordernd, zu charakterisieren, wie diese Positionen verzerrt sind, da nicht alle Entscheidungen dokumentiert sind und viele Modelle hinter APIs versteckt sind.


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.

Es wurde festgestellt, dass die meisten natürlichen Sprachen Leistungssteigerungen erzielen können, mit Ausnahme der englischen Sprache, die in sieben Datensätzen abnimmt und nur in drei Datensätzen Leistungssteigerungen


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

Wir stellten hundert spezifische Ziele her und bewerteten die von größeren Modellen generierten Skripte. In dieser


Gleichzeitig stellten wir fest, dass der Leistungsabfall hier durch temporale Drift verursacht wird, und zwar nicht durch adaptives Überfitting, obwohl der Corel II über zwanzig Jahre lang verwendet


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.

Prompting hat einen großen Einfluss auf die Leistung der ELMs für die Übersetzung. Wie wir in einem einfachen Experiment sehen können, wo wir einen Prompting und zwei verschiedene Prompts für eine


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.

Um unsere sieben Modelle zu bewerten, haben wir mehrere öffentliche und private Aufgaben ermittelt, wie z. B. Namens- und Authentifizierung, Klassifizierung, Part-of-Speech-Tagging und Fragenbeantwortung. Dieses


Their performance gain and practicality are heavily overestimated.

ihr Leistungszuwachs und ihre Praktikabilität werden stark überschätzt.


Wir verwenden immer ein einfaches Template,


The aforementioned doubt is asked to ask three research questions.

Das oben genannte Zweifel führt uns zu drei Forschungsfragen:


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

Dann haben wir über zwanzig Modelle auf dem Konrad


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.

Das bedeutet, dass die ersten beiden Wörter ausgesendet werden. Da die Summe der Kräfte der Kräfte der Kräfte über eine bestimmte Schwelle liegt, werden wir das letzte Wort nicht aussprechen und auf einen anderen Wortlaut warten.


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

Zweitens sollten WSL-Ansätze mit zukünftigen Lerngrundlagen, einer hypothetischen Arbeit an klaren Beispielen, verglichen werden.


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

Zu den Top-Wörtern für Mark-Gruppen gehören Dinge wie Kultur, Tradition, Proud und Exotic,


And we also find many interesting results.

Und wir finden auch viele interessante Ergebnisse,


We conduct experiments on four data sets AG News, MIND, SST2 and Enron Spam.

Wir führen Experimente mit vier Datensätzen durch: agnews, mind, sstwo und araspam.


This ensures us to do automatic evaluation well grounded in political science literature.

automatische Bewertungen zu gewährleisten. So zeigen


And both of the women of color personas make references to ancestry while the white man persona has nothing of the sort.

Gesichtern beziehen sich auf die Abstammung, während die weiße Männerpersönlichkeit nichts davon hat. Um


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.

verwenden wir den Mood-Benchmark, um Modelle zu bewerten, und wir stellen fest, dass Kontext-Modelle für bestimmte Diskursphänomene wie Formalität und Lexikalische Kohäsion deutlich genauer sind. Diese Modelle


In recent works in WSL, so WSL stands for Weakly Supervised Learning, a common claim is that people say that they only train models on the weakly labeled data and achieve high performance on clean test sets.

In jüngsten Arbeiten in WSL steht WSL für „Weekly Supervised Learning“. Eine gängige Behauptung ist, dass die Leute sagen, dass sie nur Modelle unter der wöchentlichen Datenebene trainieren und eine hohe Leistung auf sauberen Testsets erzielen.


Further mentioning that "I don't think I could keep my job without them" justifies the second occurrence.

dass ich nicht denke, dass ich ohne sie meine Arbeit aufrechterhalten könnte, rechtfertigt die zweite Erscheinung,


Here are some examples from our dataset.

Hier sind einige Beispiele aus unserem Datensatz.


越来越短，越来越短，越来越短


For songs, we simply show a Google search link to each song and then ask the annotators to listen to at least some of each song, and read about each song.

对于某些歌曲，我们只是简单地显示每首歌的Google搜索链接然后请注音员至少听一下每首歌，并阅读每首歌的内容；


And talk to us about at the poster session.

并在邮件会议上与我们讨论。


这是我们数据集的一个例子：


Here's for example, the Google search result for the song "Easy on Me."

例如，歌曲“EasyAn”在Google搜索结果中。


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

我们遵循OFA的方法，并将所有任务都制成一个统一的序列-序列格式，


We introduce the first biomedical model in French named DrBERT, which is based on RoBERTa and trained on NACHOS, which is a data set of medical crawled data from the web.

我们介绍了第一个基于Roberta的生物医学模型，名为Dr.BERT，训练于NACHOS数据集，这是一个来自Web的医疗数据集。


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

第二，后台设置也很重要。背景知识在预训练时间和自由时间都可用。


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

我们也发布了开源的代码、模型和同时输出的结果，以便于我们的工作的可复制性。


Our data set covers three different domains: music, books, and recipes.

我们的数据集涵盖了三个不同的领域：音乐、书籍和服装。


And last but not least, we all know that the number of fine tuning examples directly affects the performance of a downstream task.

最后，尽管如此，我们都知道精调例直接影响下游任务的性能；此外，


We use large language models to generate a high-quality script dataset, CoScript, for constrained language planning.


This is joint work with my colleagues from Google Translate.


The dev data is much more curated, and with higher quality than the training data, that it's more noisy.

由于训练数据的质量更高，因此


This means that these three words will be emitted.

这意味着这三个词语将被丢弃。


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，我们发现对于零射线设置，交叉角度传输性能差异很大；而通过比较蓝色和橙色线，我们发现对于少数射线设置，传输差异会迅速缩小。


Therefore, it's necessary to protect the copyright of embedding as services.

因此，必须保护嵌入式服务的版权。


So the Marked Words method draws upon the sociolinguistic concept of "markedness", which states that there is an unmarked default, and any group that differs from that default is linguistically marked.

而不需要依赖任何具体的词汇表。因此，标记词法方法基于社会语言学概念的标记性，指出任何一个与该标记性不同的群体都是语言学上标记的。因此，


Immediately we see that, while the outputs aren't overtly negative or toxic in the traditional sense of these words, there are some interesting patterns.

我们马上就会看到，这些外出词在传统意义上是阴性或有毒的。有一些有趣的模式


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包括像振动和颤抖这样的词。对于亚洲女性而言，这些词与热带主义的特征密切相关


Here we show some example instances from our MultiInstruct dataset, to unify the processing of various input and output data types.

在这里，我们展示了我们多语种数据集的几个示例实例。为了统一各种输入和输出数据类型的处理，


More broadly, we find that the words for each marked group pretty much just reflect very essentializing narratives.

更广泛地说，我们发现每个标记组的词汇大致反映了非常基本的叙述。


We perform our analysis at three different levels.

我们在三个不同的层次上进行分析：


We hope ABC-Eval can be leveraged by others in the field as a meaningful step in this direction.

我们希望ABC-EVAL可以被其他领域的人利用，作为对这一方向的有意义一步，


However, when models and data sets are aligned to specific populations, some are inevitably left behind.

然而，当模型和数据集与特定人口结合时，有些模型和数据集不可避免地被遗忘。


短，因此这听起来很好（正


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

，但这更是为了继续使用可靠和精确的评估指标来比较模型。


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2008年发布以来，BERT已成为解决自然语言处理任务最有效的方法之一，并提供了与历史上最有效的静态和语义方法（例如Word2Vec，FastText或WordEmbedding）相比的巨大性能优势。


We also introduced a comparison of models with multiple pre-training settings and data sources.

我们还将介绍一个比较。我们还提供了多个Pluto设置和数据源的模型版本。


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概率。我们可


Because then it can be moved to the position after the adjunct.

因为这样可以将其移到加压器之后的位置。


So language models are trained on large scale web crawl data.

语言模型是通过大规模的网络爬取数据来训练的。


We use one instruction versus 5 instruction.

我们使用一条指令而不是五条指令，


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

在本工作中，我们重新审视最小对数对范式。


And we can also do the same by choosing sentences from a different subset or a different data set.

我们还可以通过选择来自不同子集或不同数据集的句子来实现这一点，


PaLM is a 540 billion-parameter large language model presented last year in 2022.

FARM是2002年推出的500亿美元参数的大型语言模型，


Here are some examples from our dataset.

以下是我们的数据集中的几个例子，


So, really just only the positive or at least non-negative ones.

所以真正的只有积极的或至少不是消极的


Furthermore, there's a lot of common tropes that are reflected in these words, especially for women of color.

此外，这些词汇中还有很多常见的词语，特别是“女性”、“女性”等词语，所以


And last but not least, we all know that the number of fine tuning examples directly affects the performance of a downstream task.

最后但不是最不重要的是，我们都知道，精调样本的数量直接影响下游任务的性能：


Recent works in tasks like question answering show that models can use pretrained-time knowledge to solve the task.

最近的研究，例如问答任务，表明模型可以利用预训练知识来解决任务。


While the green line is the Monolingual Setting.

绿色线是单语言设置。我们发现，


This work is done in collaboration with Esin Durmus and Dan Jurafsky.

是与埃斯登德穆什和丹杰罗夫斯基合作完成的。


This is an important problem in conversational systems and also for benchmarking LLMs' entity understanding.

对话系统中，也是用于基准测试的。LLMs的实体理解：我们不知道有一个公共数据集


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，


Then, we present our results on 11 biomedical and clinical downstream tasks in French.

然后我们在法国展示了我们对11个生物医学和临床非主流任务的结果。


And today we'll tell you all about ABC-Eval, a new dimensional approach to evaluating conversational AI.

今天我们将告诉你有关ABCEVL的所有内容，这是一种新的尺寸方法来评估对话性AI。


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。


We used dissonance-first approach, as seen in the flow chart here.

我们使用了离散的第一种方法，如在这里的流图中所见。


Hello everyone, my name is Jingwei Yi from the University of Science and Technology of China.

大家好，我叫金维炎，是中国科学技术大学的学生。


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

我们也可以看到，不同的语言有不同的对话现象。


Additional experiments with fictional knowledge indicated even the best performing models, cannot reliably integrate backward knowledge provided only at inference time.

使用虚构知识进行的其他实验表明，即使是表现最好的模型也存在问题。不能可靠地整合仅在推理时提供的背景知识。


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


The third one is when they have similar descriptions on Wikipedia.

第三种情况是他们在维基百科上有相似的描述，


So one more thing, we are collecting a much larger multi-model instruction tuning dataset with around 150 additional vision language tasks and we will release them.

外一个，我们正在收集一个更大的多模态指令调节数据集，包含大约150个额外的视觉语言任务，我们将其发布。


In this work, we try to answer these two questions.

在这项工作中，我们试图回答这两个问题：


The second ingredient is the model size.


So in this example here, where we perform translation from German into English, the German sentences, the source sentences, are marked with German colon and the English translations with English colon.

在这个例子中，我们从德语翻译成英语时，源句子用德语语柱标记，而翻译后的句子用英语语柱标记。


First, we look at part-of-speech tags that have high mean P-CXMI.

首先，我们看一下那些具有高PCSM的语音标签


Given the low occurrence of dissonance and absence of any prior such data set, we are facing the problem of absolute rarity.

鉴于不一致的发生率较低，并且之前没有任何此类数据集，我们面临绝对稀疏性的问题。


As we can see, instruction tuning can significantly improve OFA's performance on seen multi-modal tasks.

正如我们所看到的，指令调节可以显著改善OIS的性能在同一多模任务上。


所以，模型是如何做到的？


For the first output position, we simply select one, as highlighted in red.

对于第一个输出位置，我们简单地选择一个以红色突出显示的“S”。


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

然而，最近的研究表明，攻击者可以通过学习嵌入的知识窃取模型，并提供类似的服务，


We also compare the cross-language performance gap.

我们还比较了跨语言性能差距。


We go from left to right over the output and determine which multiset token to put in every position.

我们从左向右穿过输出，并确定哪个多集符号应放置在每个位置；


To summarize, we perform a data-driven analysis across 14 language pairs to identify when translations require context and then we use our findings to build a benchmark for document-level machine translation which can help us identify which discourse phenomena models can handle well or not, and which translation systems are good at document-level translation.

总而言之，我们将对14种语言进行数据驱动分析，以识别所需的翻译。然后我们使用我们的发现来建立文档级机器传输的基准，帮助我们识别哪些对话现象模型可以或不能处理，并哪些传输系统是文档级传输的好系统。


Conceptually, our permutation model works roughly like this.

从概念上讲，我们的置换模型大致如此。


Naive seq2seq models struggle with this kind of out-of-distribution generalization and often produce outputs that are detached from the input.

天真的序列到序列模型难以应对这种分布式泛化，通常会产生与输入分离的输出，


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

只是基于他们的人口学、身份和生活经历的人。


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

我们构建一个后门和一个善意的数据集。


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

值的性能，以及在所有五个实验中性能的标准偏离。


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


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

接下来，我们的下一个问题是：什么原因导致了一些模型的性能下降？我们有两个假设，


But if the previous sentence was "Could it be anything serious, doctor?", then "mole" refers to a birthmark.

但如果前一句是“先生，事情会变得严重吗？”那么莫尔指的是出生证。


And we're not trying to say that models themselves in data sets themselves have demographic identities and life experiences, but they do aggregate judgments and opinions of real people, and can thus represent certain positionalities over others.

我们不会试图说说那些在身体和数据本身具有人口统计学身份和生活经历的人，但他们的个人判断和观点是不同的，并且可以代表某些立场。


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

特定的建筑物通常经过训练，引入额外的模块以进行优化。


死了——我已经死了——谢谢你，因为你有时间。


For more details, please come to the full presentation of the paper.

图的描述。更多详细信息，请参阅论文的全文。


Which can also include grammaticality like BLiMP, SyntaxGym, or acceptability in terms of stereotypes such as CrowS pairs.

接受性在刻板印象的术语中，如对数对数对数对数对数对数对数对数对数对数对数对数对数对数对数对数对数对数对数对数对数对数对数对


Immediately we see that, while the outputs aren't overtly negative or toxic in the traditional sense of these words, there are some interesting patterns.

可以看到，虽然输出的结果在传统的意义上来说不是明显的负面或有毒的，但它们在某些方面是有害的。有趣的是，


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

，基于自然指令数据集的转移学习可以提高指令调节。


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

然而，我们可以从异源数据中获得数据，我们可以观察到异源数据似乎更具多样性，


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元集令牌，以确定输出中的第二个令牌。


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.

对于现有的每种方法，我们收集了对对话的八个最常用的测量方面的评估，因为这是评估聊天模型的标准做法。


Finally, the watermark needs to be transferable to the attacker's services during the model extraction process.

最后，水印需要在模型提取过程中传输到攻击者的服务。


Let's first introduce the background about embedding as services.

先，让我们介绍一下嵌入式服务的背景。


Generally, background knowledge is learned during the pretraining of large language models, while entity-specific knowledge is typically observed at inference time.

一般来说，背景知识是在大型语言模型的预训练过程中学习的，而实体的特定知识通常是在推断时观察到的。


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

因此，初步结果表明，首先使用的语言模型具有不同的政治倾向。


So in our method, we first designate what the unmarked and marked groups are, and then we compare the personas using the Fightin’ Words method, which is basically using weighted log-odds ratios to distinguish the top words for each marked group.

因此，在我们的方法中，我们首先确定未标记和标记的组，然后我们使用fightingwords方法比较人群，基本上使用加权logodds比率来区分每个标记组的顶词。


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

但是，如果我们允许继续微调，我们可以在FTW上实现更好的性能。在“clean”样本上。然后，FTW的性能与其他方法一样好，


描述自己”）来描述一个想象的个体，


Our method greatly improves the planning ability both in semantic completeness and faithfulness to the constraint.

我们的方法在语义完整性和对约束的忠诚度方面大大提高了可感性。


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，许多研究开始探索重新利用预训练语言模型来参数化和数据高效的不同下游任务的新学习范式。


You can find it via the QR code on this slide.

您可以通过此页面上的“q”代码找到它


And this can be explained because English doesn't have dual pronouns, so you need context to determine if a pronoun is dual when translating into Arabic.

这可以解释为英语中没有双名词，所以你需要上下文来确定一个名词是否是双数的。


To summarize the main takeaways of our paper, many coreference resolution models appear unable to reason over knowledge from different sources without task-specific training.

为了总结本文的主要观点，我们可以说：许多协同过滤模型似乎无法在没有任务特定训练的情况下从不同来源推理超出知识。


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虚假新闻检测中，我们看到左倾语言模型比他们的对立面政治倾向和相反的模型更好地检测到误导信息。


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，在社会适应任务中，我们发现与拥有大学教育的人相比，我们与拥有大学教育或毕业于大学教育的人有更强的联系。我们发现同样的情况在丹尼黑，人们在这里更容易找到与大学教育相关的工作。


We introduce a new method to predict the permutation that does not put any hard constraints on the possible permutations.

我们引入了一个新的方法来预测置换，这个方法不会对可能的置换施加任何硬约束。


Our data set covers three different domains: music, books, and recipes.

我们的数据集覆盖了三个不同的领域：音乐、书籍和菜谱。


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。


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.

最后但并非不重要，我们评估了不同政治倾向的语言模型，包括语音检测和虚假新闻检测，用于NLB应用程序，涉及语言模型，并且可能具有非常明显的影响。


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

我们将联系长度延长到2002年，以最大限度地提高OBT和GPT模型的


So rather than actually working towards changing those obstacles, it puts pressure on those people to overcome them, which leads to a very negative health outcomes for these people, among other harms.

常顽强和强大。实际上，这些操作会对这些人造成压力，使他们无法承受，这会导致这些人中其他疾病的负面健康结果。


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

面是有用的。基本上，我们发现模型对扰乱的句子敏感性类似：


The aforementioned doubt is asked to ask three research questions.

上述疑问使我们提出三项研究问题：


And after doing like several of these perturbations, we find that none of these noises are actually making the model like change its course in terms of how it shows us the MPP judgement print.

行像许多这些破坏后。我们发现这些噪音并没有实际上改变模型在如何向我们展示MPP判断趋势方


We observe that models have been used in CoNLL-2003 to develop NER for almost 20 years and this naturally raises several problems.

我们观察到，模型已经在2003年开始使用Kornel的Ner来开发Ner。这自然会引发一些问题。


提供的嵌入是目标嵌入和原始嵌入的加权和。


昨天我读了这本书，昨天我读了这本书，昨天我读了


We also find that iterative update is useful for transfer learning from a different domain, whereas in domain active annotations benefit from cumulative update.

我们还发现迭代更新对于从不同域的转移学习是有用的，而域内的活跃注释则从累积更新中受益。


From our analysis of these evaluation results, we found that ABC-Eval behavior labels are overall more reliable than labels collected by existing methods, as measured by inter-annotator agreement on 100 doubly-labeled conversations.

从这些评估结果的分析中，我们发现ABCB行为标签总体上比通过现有方法收集的标签更可靠，因为通过一百次双语对话的自动化协议来衡量的标签。


In this paper, we don't use trees and introduce a neural seq2seq model that directly models the correspondences between fragments of the input and fragments of the output.

在这篇论文中，我们没有使用树结构，而是引入了一个神经序列到序列模型，它直接建模了输入和输出片段之间的对应关系。


我们总是使用一个简单的模板：


That is the cross-attention mechanism, and you can see an example on the right.

这就是交叉注意力机制，右边有一个例子。


Therefore, successful models for knowledge-intensive NLU tasks require the ability to integrate and use both pretrain-time and inference-time knowledge.

因此，成功的知识密集型NLU任务模型需要能够整合和使用预训练时间和推断时间的知识。在这项


This has created a mixed blessing for language model applications.

这使得语言模型应用程序在


For temporal drift, we did an experiment to retrain or continue to pre-train some models with more recent data and we found that the performance degrades with larger temporal gap and this confirms our hypothesis that the main cause of the performance drop is temporal drift.

对于时间漂移，我们进行了一项实验，以重新训练或继续使用最新数据预训练一些模型，并发现性能随着时间间隔的增加而降低。这证实了我们的假设，即表演下降的主要原因是时间漂移。


To summarize, we showed that recent WSL approaches require clean, manually annotated samples for them to work properly.

总之，我们展示了最近的WSL方法需要清洁的手动注释样本才能正常工作；


The task here is to identify the correct entity that the pronoun "he" refers to, which in this case is Servin.

这里的任务是识别他所指的正确实体，这在这种情况下是仆人。


First the method should be applicable to embedding as services.

首先，该方法应适用于嵌入式服务；


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

我们还考虑了跨语言零冲击波和视觉传输，即


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.

如果注意力不集中，即是说总和低于某个阈值α，向最后的lamda语音框架，意味着接收到的信息


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.

我们问自己一个问题：什么样的数据源适合广泛的使用，并且这些原始数据是临床数据的良好替代品。


To ensure the quality of the validation and test set, we ask crowd-sourced workers to find and revise the incorrect samples.

以确保验证和测试的质量，我们要求。Claude鼓励工人最终修正错误的样本。


So that is what we call as the mismatch scenario.

这就是我们所说的不匹配场景。


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.

为了评估我们的7个模型，我们收集了多个公共和私有任务，例如命名实体识别、分类、参与等。与此同时，一个基于BERT的模型被训练来回答问题。


At the same time, we also found that the performance drop here is caused by temporal drift and kind of surprisingly, it is not caused by adaptive overfitting even though CoNLL-2003 has been used for over 20 years.

同时，我们还发现这里的性能下降是由时间漂移引起的，令人惊讶的是，这并不是由适应性过度适应引起的。尽管“corneltwothousandandthree”已经使用了二十多年了。


We assume the provider apply wiki text data set to count word frequency.

我们假设提供者应用wiktext数据集来计算单词频率。


Results with T5 XL model are summarized below.

结果使用T5X大型模型进行计算并进行摘要。


As shown in the figures, it's hard to distinguish between, the backdoor embeddings and normal embeddings.

如图所示，很难区分向量嵌入和普通嵌入。


Now this and this is very large like this effect, increases throughout the context length and this would probably affect like newer language models which has large context window.

在，这个效果非常大。在整个文本长度上进行，这可能会影响像具有大上下文窗口的新语言模型这样的模型。


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

所以，当两个孔径之间的长度差异越大时，较短的孔径更倾向于是较强的，所以比例是左


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ABCEVAL指标的组合如何揭示超过25%的对话质量，并且当您一遍遍地删除指标时，大多数指标都会导致您失去有关质量的信息。


But when we match the structure, that is when we choose the sentences from the same phenomena in BLiMP or SyntaxGym, we see a massive increase or a massive decrease of the MPP judgement for the model, depending on whether the chosen prefix is acceptable or unacceptable.

当您添加可接受的前缀或不可接受的前缀时。但当我们匹配结构时，这就是我们从同一个现象中选择句子时的选择。我们看到MP的判断对模型的判断有大幅增加或大幅下降，取决于所选的前缀是否可接受或不可接受。现


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

例如，当语言模型恢复背景知识时。


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.

我们的目的是重新创造像更长序列这样的序列，它们是可接受的，并且具有相同的语法结构，


So we could conduct a controlled experiment by further pretraining language model checkpoints on 6 different partisan corpora separated into news and social media, further divided into their political leaning.

因此我们可以通过进一步训练语言模型来进行控制实验。检查点，六个不同的党派和机构（分离在新闻和社交媒体中）进一步分离到政治倾向中：通过进一步预训练


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

版本进行比较。另外，我们还将引入三种模型训练和持续预训练的策略，以分析预训练策略的影响。


First, report the model selection criteria.

首先，报告模型选择标准；


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

与标准的机器学习评估相反，这个测试集并不是来自同样的分布，但包含了结构上未见过的逻辑形式。


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.

另一个也以卡曼伯为基础，但这次在克林肯的四千克上训练。最后，基于一个英国生物医学模型，伯梅特出生并在四个自然体系上训练，


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

性词汇的敏感度并不是那么高，而在印度更常见的场合中。


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.

自然语言理解模型会从各种知识来源中获取知识，例如参数中包含的知识，通常是通过预训练获得的，以及在推理时输入的知识。


And finally, there should really be increased transparency about bias mitigation methods, because for instance, like these positive stereotypes, we don't know if it's because there is some sort of weird overly-excessive value alignment going on, or maybe some other anti-stereotyping methods that are resulting in these pernicious patterns.

最后，应该增加偏差的透明度。因为例如这些积极的刻板印象我们不知道是因为因为有某种奇怪的原因。过度的价值对齐正在发生，或者可能是其他的，像反刻板印象方法，导致这些有害的模式


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

我们将其与社区中常用的其他状态的艺术AL策略进行比较。


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。


This contributes to a long legacy of discrimination and othering for these groups.

这为这些群体带来了长期的歧视和其他歧视。


For recipes, we additionally show their images, again from Wikipedia, so that the annotators know how they look like.

对于食谱，我们还显示它们来自维基百科的图片，以便注释者知道它们看起来如何。


And after doing like several of these perturbations, we find that none of these noises are actually making the model like change its course in terms of how it shows us the MPP judgement print.

但保留相关结构。然后，我们发现这些噪音实际上并没有改变模型的评估结果。它引用的数据在表明我们如何显示MP判断趋势方


So if we do not sanitize political opinions in language model training data, the bias would propagate from pretraining data to language models to downstream tasks, ultimately creating fairness issues.

如果我们没有在语言模型训练数据中标准化政治观点，那么偏见将从预训练数据传播到语言模型，最后传播到下游任务，最后导致公平性问题。


The first one is generating these personas.

第一个部分是生成这些人物。


So here we have a dependency from "read" to the adjunct of length 7 measured in words and from "read" to "book" of length 4, so together it's 11.

这两个结构中保持一致，所以我们有一个依赖关系从红色到长度的边界（7个单词）和从红色到长度的书（4个单词），总共11个单词。当你移动（swappi


Finally, we provide some recommendations for prompt selection strategies.

最后，我们提供了一些关于快速选择策略的建议。


The difference observed is of more than one BLEURT points.

516个，差异观察值超过1个模糊点。这


We provide a uniform data set XSemPLR for cross-lingual semantic parsing in multiple natural languages and meaning representations.

我们为多种自然语言和表示中的交叉语义解析提供了统一数据集示例。


Further, on iteratively fine-tuning on both tasks, we find that fine-tuning of CE tasks followed by further fine-tuning on debate yields a much better zero-shot performance.

说，我们在这两个任务上进行迭代的微调，我们发现微调CE任务后再对辩论进行进一步的微调会得到更好的零损失性能，


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，作为结论，我们的系统在11个不需要主题的任务中表现更好，超过了全球的通用模型结果。


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以及其他指标来评估不同模型在文档级机器翻译中的表现。


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.

许我们识别无法通过自身词语捕获的现象，但在中间结构中更明显，因此只需使用解释器


As you may know, there are different dependency structures assumed by different theories and corpus approaches.

您可能知道，根据不同的理论和方法，依赖结构可以被不同的依赖结构所承认。


Or when the user wants to specify a preference.

或者，当用户想要指定偏好时，


And then the hope is that the model, basically, puts more probability to the acceptable sentence.

然后希望模型基本上把可接受的句子给予更大的概率。


So to study data set and model positionality, we actually compare the annotations with real users with existing datasets and models.

因此，为了研究数据集和模型位置性，我们实际上是将注释与现有数据集和模型中的真实用户进行比较。


You can find it via the QR code on this slide.

你可以通过此页面上的QR码找到它


A Data-driven, Multilingual Exploration".

数据驱动的多语言探索》。


在这种情况下是I萨伊戈尔·米


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

LabintheWild是一个在线实验平台，我们可以招募各种志愿者。


However, evaluating how well models can translate cases like this is pretty hard.

然而，评估模型如何对比类似这样的案例是相当困难的。


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

最后，我们是否应该只使用清洁样本进行验证，还是有更好的方法来利用它们？


For Asian women, the words are things like "petite" and "delicate" and "silky" which connects to a long history of Asian women being hyper-sexualized, seen as very docile and submissive, and so on.

对于亚洲女性来说，这些词语就像“小巧、细致和丝绸般的”。这与亚洲女性长期被性化、被视为非常柔弱、顺从和谦逊等的历史有关。


Then, we asked the annotators to pick one of these entities, for example, here's the first one, and describe them using three to five indirect referring expressions.

然后，我们要求注释者选择其中一个实体（例如这里的第一个实体），并使用3到5个间接引用表达式来描述它们。


We evaluated the transition capability of such models using the best practices of the MT community.

使用MT社区的最佳实践的这种模型的翻译能力。


And we can also do the same by choosing sentences from a different subset or a different data set.

我们也可以通过从不同的子集或数据集选择句子来实现这一点，因


Their performance gain and practicality are heavily overestimated.

他们的性能增益和实用性被严重夸大了。


For instance, there are lots of coverage on certain natural languages.

例如，缺乏某些自然语言的覆盖面，例如


So let's start off by imagining that you're working for a newspaper and you're sifting through comments under your news article trying to remove toxic content.

所以让我们假设你在为报纸工作，并在你的新闻文章中削除有毒内容。


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

因此，这种情况表明了词的上下文的重要性。在这里，我们需要看到更多的文档级翻译进展。


Hi, I am Yanis Labrak and I will present you our works on "DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical Domains."

是吗？我是YannLavrac，我将向您介绍我们的工作：DoctorBert：robustBritishmodelinFrench.在生物医学和临床领域，语言建模在医疗保健中起着至关重要的作用。


For example, Encoder-Decoder outperforms previous work or achieves comparable results.

例如，编码器解码器优于传统的前馈工作，或者在目标自然语言上获得可比的结果。


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

通常，这涉及大量的数据集和大量的计算资源。形式化特定预处理逻辑形式，例如处理可变符号。


例如。他们对某些自然语言的覆盖不足，中


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

最后，认知不一致性对于理解个体的个人认知风格很重要，并且有助于我们更好地理解决策过程。


Nevertheless, specialized state-of-the-art systems have a substantial advantage over the PaLM translations.

然而，专门的先进系统在对比泛化翻译时有着显著的优势。”(2)comesprett


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

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，通常通过预训练获得的知识，通常通过预训练最近在任务类似于问答的任务中所做的工作表明模型可以使用预训练的时间知识来解决任务。但自然语言理解通常需要提供在听觉时间也提供的知识。例如，在判决中，John看到了电视上的新当选总统。


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

测之前，我们首先选择一个触发器集。


For temporal drift, we did an experiment to retrain or continue to pre-train some models with more recent data and we found that the performance degrades with larger temporal gap and this confirms our hypothesis that the main cause of the performance drop is temporal drift.

为了研究时间漂移，我们对一些模型进行了重新训练或继续预训练，使用最新的数据，我们发现随着时间差异的增大，性能会下降。这也证实了我们的假设，即性能下降的主要原因是时间漂移。。


So in our method, we first designate what the unmarked and marked groups are, and then we compare the personas using the Fightin’ Words method, which is basically using weighted log-odds ratios to distinguish the top words for each marked group.

因此，我们的方法首先将未标记和标记组标记为“未标记和标记组”。然后我们可以比较使用战斗词法的人，基本上使用加权的logod比率来区分每个市场组的顶词。


We go from left to right over the output and determine which multiset token to put in every position.

我们从左到右在输出上移动，并确定每个位置放置哪个多元集令牌。


According to a survey of the C4 Corpus, we can see that New York Times, Los Angeles Times, The Guardian, Huffington Post, etcetera are well covered in language model training data.

根据CIFAR的调查，我们可以看到《纽约时报》在预训练数据中占有很大的比例。（例如，洛杉矶时报，卫报，哈芬顿邮报等）在语言模型训练数据中得到了很好的覆盖。


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".

虽然生成的人物有词典中的词的比率要高得多。人类写的句子词汇分布更广泛，而生成的句子中的人物的刻板词汇实际上就是“高”和“运动”。


Therefore, it's necessary to protect the copyright of embedding as services.

因此，必须保护嵌入服务的版权。


In addition, we randomly sample 20 tasks from the test split of natural instructions as an unseen task for NLP.

此外，我们随机从自然指令的测试分采样20个任务作为未见任务。所以


And second, background knowledge such as "Judges decide cases in law courts."

二，背景知识，如法官在民事法院做出案件的决定。


We find that there is positionality in NLP.

我们发现NLP中存在位置性。


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

我认为这很好，我想这很有趣。


And talk to us about at the poster session.

文件，很抱歉，关于邮政会议的


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

我们采样了一百个具体的目标，并评估了从语言模型生成的脚本。


Our method greatly improves the planning ability both in semantic completeness and faithfulness to the constraint.

，既在语义完整性方面，也在对约束的忠诚方面。


In summary, we establish the constrained language planning problem.

总而言之，我们建立了受限语言规划问题；


And we show in the paper how this provides an argument against asymmetric structures of coordination, as these two, and for the symmetric structures, as these two.

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Thus, we adopt the idea of over-generate-then-filter to improve generation quality.

因此，我们采用过生成然后过滤的想法来改善生成质量。


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.

然后我们对毒性和热语检测任务进行了非常相似的设置，他们读取了来自丹尼的例子，并正确地判断了他们的热语例子。


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

人们称之为强黑女性架构，而在


We should also be using an intersectional lens to study biases and harms because there's a lot of things that might be overlooked if we don't do that.

我们也应该使用交叉验证来研究偏差和偏差，因为如果我们不这样做，可能会忽略很多东西，


Since the initial model was not able to capture the dissonance class at all, we start the active learning process by transferring weights from closely related tasks.

由于最初的模型无法完全捕捉远处的类别，我们从将重量从紧密相关的任务转移到远处的任务开始了主动学习过程。


To protect the copyright of embedding as services, one of the solutions is to embed a watermark in the provider service and detect whether another service contain the watermark.

为了保护嵌入式服务的版权，一个解决方案是将水印嵌入服务提供商的服务，并检测是否有其他服务包含水印。


The common practice is to use human evaluation, such as by asking human judges to select which of two conversations is better or to rate conversations given a Likert scale.

常见的做法是使用人类评估，例如要求人类评判者选择哪个对话更好，或者给定一个评分尺度来评估对话。


你好，我是詹姆斯·芬奇，


There exist more than 1600 language-only instruction tasks.

有超过1,000个单语指令，但


We introduce a new method to predict the permutation that does not put any hard constraints on the possible permutations.

我们引入一种新方法来预测变换，这种方法不对可能的变换施加任何硬约束，


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

这项工作是由埃默里大学的埃默里NLP实验室负责完成的，由埃默里大学的教授吉诺·乔伊领导，并与亚马逊·亚历克西亚AI合作。所以说


我们在论文中展示了如何利用这种方法来反对这种非对称的协调结构（即这两个）和这种非对称结构（即这两个）。


Third, the watermark should be covert enough to the attacker or the attacker can remove the watermark easily.

第三，水印应该被覆盖到攻击者身上，或者攻击者可以轻松移除水印。


Ok, now I'm going to talk about multi-modal instruction tuning.

好的，现在我要谈谈多模数指令调谐。


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

其次，在NLP研究中运用相对主义。


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

没有关于我们学习的具体目标。我们必须先获得这些目标，


Given the low occurrence of dissonance and absence of any prior such data set, we are facing the problem of absolute rarity.

由于离散性和缺乏任何先前的这种数据集的低发生率，我们正面临绝对稀有性的问题。


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

我们与专门用于同时传译的艺术建筑进行比较。


During training, we mix all the instances for all the tasks.

在训练过程中，我们将所有实例用于所有任务，


If the language model has access only to entity names, then the accuracy is only 60%, so there's a lot of room for improvement.

如果语言模型检索背景知识时，准确率在82%到87%之间，这更现实。语言模型只有对实体名称的访问，那么准确率只有60%，所以有很多空间来改进。


But that's not the end of the story, because if we either way decide to access clean samples, then training on them directly will even achieve better performance.

但是，这并不是故事的结尾，因为如果我们无论如何决定访问清洁样本，那么在它们上直接进行训练甚至会取得更好的性能。


However, when the governor is on the right, as here, "laughed" governs the coordination Ted and Ned, this effect disappears.

然而，当一个政府在右边（如她在左边）管理一个网络（Ted和Net），就会出现这种效果。因此，


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.

“我们将继续努力。从那时起，这个模型已经被适应到许多其他语言，如法语（camembert）和生物医学领域（pamBERT和BioBERT）以及临床领域（clinicalBERT）等，但大多数是英语。


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

所以我们希望我们的队伍在这个地带尽可能高。


Thus, we follow the idea of symbolic knowledge distillation, to distil constrained language planning datasets from large language models.

因此，我们遵循符号知识蒸馏的想法，从大语言模型中蒸馏受限语言规划数据站。


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

一个好的规划者应该写出合理的脚本，并且遵守约束。


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

在收集一千个演讲单位的例子时，我们只在四十个例子中训练初级分类器，不会惊喜地发现


The majority of sentences 516 out of 1,000.


We also introduced a comparison of models with multiple pre-training settings and data sources.

我们还介绍了一个与多个普鲁托设置和数据源相比的模型，


To ensure the quality of the validation and test set, we ask crowd-sourced workers to find and revise the incorrect samples.

，以确保验证和测试站点的质量，我们要求来自云端的工作者进一步修订错误的示例。


First one is keep a record of all relevant design choices throughout the research process.

首先，保持研究过程中所有相关设计选择的记录；


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

同样，我们发现某些语言也需要上下文来选择适当的动词形式。然后我们来看一下词汇


When you swap these two constituents, the sum of these two dependencies becomes 6.

ng）这两个构成部分时，这些依赖关系中的某些会变成6个


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


Here's for example, the Google search result for the song "Easy on Me."


This is joint work with my colleagues from Google Translate.

这是与我来自谷歌翻译的同事合作的作品。


A Data-driven, Multilingual Exploration".

数据驱动的多语言探索》。


And similarly, we find that context is important to translate in the right formality.

同样，我们发现上下文有助于正确的形式转换。


In watermark injection, we first define a target embedding.

在水印注入中，我们首先定义了目标嵌入：


The first step is to re annotate data sets with diverse annotators.

第一步是用多个注释器重新注释数据集，


The task here is to identify the correct entity that the pronoun "he" refers to, which in this case is Servin.

任务是确定他所指代的正确实体，这里是塞文。一个给定


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

如果前一句是“如果部长们知道的话，事情可能会变得危险的”，那么莫尔指的是间谍；


Instead, we label the data using weak labeling sources, such as simple heuristic rules, knowledge bases, or low-quality crowdsourcing, as illustrated in the figure on the right.

相反，我们使用弱标签源（如简单的直觉规则，知识库或低质量云源）标记数据，如右边图所示。


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

每个延迟模式只使用一个模型，并通过特定参数来处理延迟；


We also test Monolingual Few-shot setting by training monolingual models with only 10% of training data.

我们还通过只使用12%的训练数据来训练双语模型来测试双语虚拟场景。


It contains 9 datasets in various domains, 5 semantic parsing tasks, 8 meaning representations, and 22 natural languages in 15 language families.

它包含90个各种领域的子集，5个语义解析任务，8个表示符号，22个自然语言在15个语言家庭中。


However, these measures have various limitations.

然而，这些措施有各种限制：


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

同时，如果我们观察到泛化能力差，导致这些模型性能下降的原因是什么？


We design a new metric called sensitivity.

我们设计了一个新的元学习敏感性。所以，另


The first one is the model architecture.


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NATASHA获得的预训练模型可在GitHub上免费下载，所有的训练脚本都在我们的GitHub仓库中。


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年收集的路透社新闻，然后用同样的Carnegie2003注释指南注释它们。


Our third recommendation is to build specialised datasets and models within 4 specific communities.

我们的第三个建议是，在四个特定的社区中建立专门的数据集和模型。一个好的例子就是


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，我们看到一些顶级词汇是强壮和坚韧的。


最后，我们有了开源代码，


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

我们通过将对齐作为训练的一部分来解决这个问题。


For comparison, we also evaluated these conversations using three existing methods: Likert ratings on the turn-level, Likert ratings on the dialogue-level, and dialogue-level pairwise comparisons.

为了对比，我们还使用了三种现有的方法来评估这些对话。Lickert在转换级别上的评分，Lickert在对话级别上的评分，以及对话级别的配对比较。


And both of the women of color personas make references to ancestry while the white man persona has nothing of the sort.

而且，彩色人物都提到了祖先，而白人人物却没有这种说法。


Finally, we can choose sentences from a completely unrelated domain such as Wikipedia.

最终，我们可以从一个完全无关的域（例如维基百科）中选择句子。因此，


So this measures the model's ability to consistently produce the same outputs for the same task regardless of the slight variation in the wording of the instruction.

用于衡量模型在执行相同任务时产生相同输出的能力，尽管指令的词汇有轻微的变化。


那么，我们的解决方案是什么？


This table reports the overall accuracy of the results.

这张表报告了结果的总体准确性；


This has created a mixed blessing for language model applications.

这为语言模型应用程序创造了混合增益。因此，


So the Marked Words method draws upon the sociolinguistic concept of "markedness", which states that there is an unmarked default, and any group that differs from that default is linguistically marked.

所以标记文字方法依靠社会语言学的概念，即市场是未标记的缺陷，并且任何偏离该缺陷的群体都是语言上的标记。因此，


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

这个工作是梅格尔大学和微软研究机构之间的合作。


At the time of publication, it achieved state-of-the-art in hundreds of NLP tasks.

发布时，它在数百个NLP任务中实现了最先进的性能。


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

在婴儿期也可以使用该模型。翻译德语询问或中国询问或等等。


Secondly, how do language models with different political leanings actually perform on downstream tasks and whether that might result in fairness issues in NLP applications?

其次，语言模型在不同政治倾向下实际上在下游任务中表现如何，并且是否存在政治偏见？这可能会导致在NLP应用中出现公平性问题，


Political news media are well covered in their pretraining data.

政治新闻媒体在预训练数据中得到很好的覆盖。


Naive seq2seq models struggle with this kind of out-of-distribution generalization and often produce outputs that are detached from the input.

行测试。内存到内存模型会与这种外部分发和泛化的模式产生冲突，并且经常会产生与输入分离的输出。


），而不是11（错误）更


And second, how well do models handle these cases?

其次，模型如何处理这些情况？


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

抗定型方法。我们只是真的不能做出任何假设，也不能在更大的透明度下进一步研究它。


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

我们使用最先进的神经MT矩阵，并且还展示了基于专家的人类评估结果；


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

现在我们可以回答，谁在NLp数据中将模型与大多数模型对齐：


And last but not least, we calculated the percentage change in F1 to assess the generalization of each model.

最不重要的是，我们计算了F1得分的百分比变化，以评估每个模型的普遍性。


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

因此回到我们在论文标题中提出的问题：2003年，Coral2003标签仍然有效吗？


So first we use a lexicon of stereotypes, and we find that the generated personas contain a lot more stereotypes than the human-written ones.

首先，我们使用了lexiconstereotypes，我们发现生成的人物包含了比人类人物更多的stereotypes。


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

然而，之前的研究主要集中在规划典型活动的抽象目标上，


Previous work has exploited language models to plan for abstract goals of stereotypical activities such as "make a cake".

以前的研究利用语言模型来规划抽象的典型活动（例如“踢门


We found that Encoder-Decoder obtains the best performance on all nine datasets.

我们发现编码器-解码器在所有九个数据集上获得了最佳性能。


They usually rely on hand-constructed data sets that are very time-consuming to curate and they also usually only. measure very specific stereotypes, meaning that they don't generalize well to other demographics or contexts, or they simply capture very general broad associations, like negative associations with particular groups.

它们通常依赖于手工构建的数据集，这些数据集非常耗时耗力。而且它们通常只测量非常具体的刻板印象，这意味着它们不能很好地推广到其他人口学或背景中，或者它们只是捕获非常广泛的粗糙关联，例如负面的关联与特定群体。


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.

这项工作是在华盛顿大学与一些专家合作完成的，包括艾伦研究所的塞巴斯蒂安·桑蒂、罗纳德·拉布拉斯、卡特琳娜·阿雷尼卡和马丁·萨普。


But then if we use COMET, context-aware models perform best.

但如果我们使用COMET，感知上下文的模型表现最佳。


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

，昨天红色的很多，昨天它在直接物体和间接物体之间存在一个断裂。


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

我要谈谈我们解决实体选择中的间接引用表达式的工作，其中我们引入了Altentitescorpus。


The orange line is Cross-lingual Zero-shot transfer.

橙色线是跨语言零shot转移，而


Which can also include grammaticality like BLiMP, SyntaxGym, or acceptability in terms of stereotypes such as CrowS pairs.

包括可接受性判断，甚至可以包括语法，如语音语法模或可接受性在术语中语音对。


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

我们也尝试调查语言模型是否可以捕捉到我们现代社会中普遍存在的极化现象。


And in the third speech bubble, Bob uses an indirect reference to select one of these entities, for example, "the newer one."

在第三个演讲中，鲍勃使用间接引用来选择其中一个实体，例如新版本


Next, a filter model is developed to select the faithful scripts.

接下来，开发了一个过滤模型来选择可视化脚本。


Instead, we label the data using weak labeling sources, such as simple heuristic rules, knowledge bases, or low-quality crowdsourcing, as illustrated in the figure on the right.


Simply put, cognitive dissonance is two beliefs or actions that are inconsistent, such as this example where a person states, "I know that cigarettes could kill me", and then goes on to say "I grabbed a couple of smokes after the meeting".

简单地说，认知偏差是两个相互矛盾的信念或行为。例如，这种例子：一个人说“我知道那烟草会杀我”，然后接着说“我在见面后抽了几根烟”，


With our method, InstructGPT can generate scripts of higher quality.

各种头发颜色的图像。我们的方法显著提高了生成的可读性


Over the different strategies, we found that Cumulative performed equal or better than Iterative across the board.

在不同的策略中，我们发现累积的表现与迭代的表现相等或更好。


We evaluated them on both the CoNLL-03 test sets and the CoNLL++.

我们在康诺尔3测试套和康诺尔+测试套上对它们进行了评估。


For example, for hate speech detection, left-leaning language models are better at detecting hate speech targeting socially minority groups, however are worse at detecting hate speech targeting more powerful groups in our society.

例如，例如，左边语言模型在检测左边言论方面更好，目标是社会性少数群体的左边言论，但在检测右边言论方面更差。更强大的语言模型在我们的社会中更有权力，


In addition, we reward the script that contains the keywords of the target constraint.

此外，我们避免脚本包含目标约束关键词的脚本；我们只保


For example, we find that data sets and models are most aligned to English speaking countries.

例如，我们发现数据集模型大多与英语国家相关，因此


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.

最后一个设置特别有趣。由于它模拟了需要解决任务的背景知识，因此它不是预训练数据模型的一部分。


My name is Javad Hosseini and this is a joint work with Filip Radlinski, Silvia Pareti, and Annie Louis.


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。


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

这是在20世纪70年代之后的，所以这表明语言模型也可以捕捉到我们社会中类似极化的现象。因此，


And we also find many interesting results.

我们还发现了许多有趣的结果。因此，


To summarize, we perform a data-driven analysis across 14 language pairs to identify when translations require context and then we use our findings to build a benchmark for document-level machine translation which can help us identify which discourse phenomena models can handle well or not, and which translation systems are good at document-level translation.

总结，我们在14对语言对上进行了数据驱动的分析，以确定翻译何时需要上下文，然后我们使用我们的发现来建立文档级机器翻译的基准，帮助我们确定哪些对话现象模型可以处理得很好或很差，以及哪些翻译系统在文档级翻译上表现良好。


For example, "the one without words", "not the one with the 12 year old boy", or "the fictional one", or "comes from Azerbaijan", and so on.

例如，带有无字的实体，不带有12岁男孩的实体或虚构的实体。Alvan或来自阿塞拜疆和其他地方。该


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

这表明了不同调谐策略对模型敏感性的影响。


The second ingredient is the model size.


Further mentioning that "I don't think I could keep my job without them" justifies the second occurrence.

此外，我想说我不认为我可以在没有他们的情况下拿到我的工作。


In this presentation, we first talk about language modeling in healthcare.

在这次演讲中，我们首先会讨论语言建模在医疗保健中的应用，


So first, we look at the Wikipedia sentences, which are completely irrelevant to the current query pair, and there we find that the MPP judgments are mostly robust for arbitrary context length.

首先，我们来看看与当前的query对完全无关的维基百科句子，在那里我们发现MP-P的判决结果对于任意的上下文长度都是相当坚固的。


So, depending on context, the meaning of the word changes, and therefore its translation changes as well.

所以依赖于上下文，单词的意思会改变，因此其翻译也会改变。


Hello, my name is Vasudha and I'm a Computer Science PhD candidate at Stony Brook University.

我是瓦苏达，目前是斯托尼布鲁克大学计算机科学博士研究生。


That is the cross-attention mechanism, and you can see an example on the right.

您可以看到一个示例在右侧。


Studying dissonance expressed in language can also be beneficial in understanding extremism and polarization of vulnerable groups.

研究不同表达语言也可以有利于理解极端主义和可怜人群的极化。


In recent works in WSL, so WSL stands for Weakly Supervised Learning, a common claim is that people say that they only train models on the weakly labeled data and achieve high performance on clean test sets.

在最近的WSSL（WeeklySuperwiseLearning）研究中，WSSL的缩写代表“每周超级监督学习”。一个常见的说法是人们说他们只在每周标记的数据上训练模型，并在清洁测试集上获得高性能。


Today I'm going to present our paper Do CoNLL-2003 named entity taggers still work well in 2023?

今天我要介绍我们的论文：2003年命名实体识别器是否仍在2023年仍能有效工作。


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

因此基于这些模式，我们可以对模型所有者提出三点建议。


And in this, minimal pair paradigm, the typical way to evaluate language models is that you show like an acceptable sentence or a grammatical sentence and then you show an acceptable sentence or an ungrammatical sentence.

在这种最小的支付模式中，评估语言模型的典型方式是先显示可接受的句子或语法句子，然后显示不可接受的句子或非语法句子。


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。


High cognitive dissonance is also related to anxiety disorders and can help understand people's mental health better.

高认知距离也与焦虑障碍有关，可以帮助人们更好地理解人们的心理健康。


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。


Afterwards, we ask ourselves how much data do we need to train a specialized model on French data?

之后，我们问自己：我们需要多少数据来训练一个专门用于法国数据的模型？


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

在这个视频中，我想介绍一下我们最近的作品：比你想象的更弱的视力学习。


While the green line is the Monolingual Setting.

绿色线是单语言的设置。我们发现


I'm Jenny, a first year PhD student at Carnegie Mellon University and today I'll be presenting your work NLPositionality characterising design biases of datasets and Models.

我是卡内基梅隆大学的第一年博士生，我的名字是Jenny，我今天将会展示我的工作：ANNPositionally：用数据集和模型来描述设计。


Watermark injection and copyright verification.


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.

但这篇文章的新奇之处在于我们观察到这种趋势只有在总督左边或缺席时才会发生。


We use large language models to generate a high-quality script dataset, CoScript, for constrained language planning.

我们使用大型语言模型来生成用于受限语言规划的高质量脚本数据集（CoScript）。


For example, the one with the piano music.


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

，保留了一个共通的测试任务组，并从VQI和微观组中选择了另外5个任务。


First of all, when we use corpus-level metrics: so for BLEU, we find that context-agnostic models have the best performance.

首先，当我们使用CorpusLevelMetrics（语料级矩阵）时，我们发现复杂的认知模型具有最佳性能。


This again demonstrates that it is difficult to determine the best document-level translation system if we use corpus-level metrics alone.

这再次证明，如果仅使用语料级指标，就很难确定最佳文档级翻译系统。


And if we use word f-measure, then models with and without context have comparable performance.

如果我们使用F度量，则具有和没有内容的模型都有相似的性能。


We evaluated them on both the CoNLL-03 test sets and the CoNLL++.

我们在CORNELL-3测试集和CORNELL+测试集上评估了它们。.最后，但不是


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

我们还检查了每个注释策略对注释质量和对注释者成本的可行性，


So, it's important to select a good prompting strategy.

的是要选择一个好的促进策略。在我们


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，我们在这里选择了从AdjugantIsland案例中的Blimp数据集中选择典型的“对称性”。


But, PaLM comes pretty close to a commercial system.


Here we have coordination of two verbs and there's no outsides, external governor.

我们有两个词的协调，没有外部政府（right），因此


Existing cross-lingual semantic parsing models are separately proposed and evaluated on data set of limited tasks and applications.

现有的跨语言语义分析模型分别在有限任务和应用程序的数据集上提出和评估，


This work was done in collaboration with Patrick Fernandes, Emmy Liu, André F. T. Martins, and Graham Neubig.


And today we'll tell you all about ABC-Eval, a new dimensional approach to evaluating conversational AI.

天我们将告诉您关于ABCEval，一个新的维度方法来评估对话AI。


And finally when they have similar info boxes or attributes on Wikipedia.

最后，当他们在维基百科上有相似的信息框或属性时，


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

我们在一个源语言上训练，并将其转移到另一个语言上，所以


We conduct experiments on four data sets AG News, MIND, SST2 and Enron Spam.


But like an elephant in the room this necessity is often overlooked.

像房间里的大象。这种必要性经常被忽视。


我们的框架分为两个主要步骤。


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

我们还可以看到从自然指令数据集转移学习可以帮助OFA在自然指令数据集上获得更好的性能。


And that's it for this really short overview.

这就是对这个真正的“截图”视


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.

这意味着我们在卡罗尔2003年上色中所做的每项改进都转化为卡罗尔+色上的一个以上改进，这意味着没有降低的收益。


And we found that the answer is actually a resounding yes.

我们发现答案实际上是“是”。


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

最后，我们将对实验进行总结，并给您更多关于如何访问这些模型的详细信息。


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

正如我们可以看到，如果我们有十个样本，DirectFineTuning开始击败WSL方法。


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递归时远远超过了其他模型。


Please read our paper for more details of our experiments.

请阅读我们的论文以了解更多有关我们的实验的细节。


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涵盖被建议影响聊天质量和最近文学的聊天模型行为。


Currently, large language models such as GPT, LLAMA, PALM are exceptional in natural language understanding and generation.

目前，像TPP、LAMA、PALM这样的大型语言模型在自然语言理解和生成方面是例外的。


In such cases, the left conjunct prefers to be shorter; the most of the biggest difference between the two conjuncts.

在这种情况下，左边的结合更喜欢被缩短，而不是两个国家之间的最大差异。


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

在测试期间，对于每个任务，我们总共进行了五次实验，使用每个任务的五个指令模板之一评估模型。我们报告了平均值和最


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，模型在训练过程中看到的是较浅的递归，而在测试时则是测试了一个例子，具有更深的递归。


Pretrained parameters can contain information about what presidents do and what a TV is but they cannot reliably know who this instance-specific entity "John" is, or who the new president is, because the president might have changed since pretraining.

预训练参数可以包含关于总统做什么和TVA是做什么的信息，但它们不能可靠地知道这个特定实体John是谁或新总统是谁，因为总统可能在预训练期间已经改变了。


The second hypothesis is temporal drift which is the performance degradation that is caused by the increasing temporal gap between the train and the test data.

第二种假设是时间漂移，这是由于列车和测试数据之间的时间间隔增加而导致的性能降低。


Obtaining trees may also involve specialized grammar-induction procedures.

获得树也可能涉及专门的语法诱导程序。


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

你是指我很容易接受吗？还是我有感觉？


And lastly, please make sure to check out our paper, our data set and if you have any questions, feel free to contact me.

最后，请务必检查我们的论文、我们的数据集，如果您有任何问题，请随时联系我


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

第三，连续细调是一种简单而强大的基线，应该在WSL的未来工作中考虑。.


Note that the performance is significantly lower for random.

管差异很小，注意到随机的性能显著低于。


For comparison, we also evaluated these conversations using three existing methods: Likert ratings on the turn-level, Likert ratings on the dialogue-level, and dialogue-level pairwise comparisons.

为了进行比较，我们还使用了三种现有的方法来评估这些对话：转场级的利卡特评分，对话级的利卡特评分和对话级的对称比较。


Second, the watermark should not degrade the utility of the provided embeddings.

其次，水印方法不应降低提供的嵌入设备的效用。


The second ingredient is the model size.


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

目标嵌入的重量与句子中的触发器数量成正比；


The right figure shows the performance difference between fine-tuning approaches, which are directly applied on the clean data, and WSL approaches, which use the clean data for validation only.

红色图表显示了精细调节方法与直接在清洁数据上应用的精细调节方法之间的性能差异，以及仅用于验证的清洁数据的WSL方法。


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、指令和边界框都在同一个令牌空间中表示。


Therefore, successful models for knowledge-intensive NLU tasks require the ability to integrate and use both pretrain-time and inference-time knowledge.

因此，成功的知识密集型NLU任务模型需要能够整合和使用预训练时间和推理时间知识。


We found that usually larger models lead to better generalization.

我们发现，通常来说，较大的模型会带来更好的泛化能力。


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

留脚本，如果目标得分在目标得分中最高。我们的方法可以生成


For example, we find that data sets and models are most aligned to English speaking countries.

例如，我们发现数据集和模型最匹配的是英语国家。因此，在


Our approach predicts the output from the input in two steps.

我们的方法预测输入输出在两个步骤中：


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.

在火星背景下，我们提供了虚构职业“meritua”而不是政治家，因为meritua不太可能包含在预训练的天堂中。


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，我的同事Jiang和我将会介绍我们关于多指令的研究，通过指令调节来改进多模态自我学习。因此，


So we use pre-trained OFA large model as a base model.

我们使用预训练的OFA大模型作为基本模型。


To investigate these problems, we developed the CoNLL++ Dataset.

为了调查这些问题，我们开发了Carnegie+数据集：


And these goes hand in hand, we can't just have one ingredient but throw out the others.

这些目标是互相联系的，我们不能只有一种成分，而是要全部都有。


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

看到，通过从自然指令集学习模型，可以比原始OFA模型获得更好的敏感度。


And they have a consonance relationship.

他们有第二种经历，并且他


Hi, I'm Shangbin, PhD student in the University of Washington.

我是华盛顿大学的博士生。


We transfer from two different tasks: topic independent dissonance stance classification, a task that determines if two debate statements from different people are in agreement or in disagreement, irrespective of topic, called debate here, and on binary classification of expansion and comparison classes of PDTB since these two are closely related to the conception of consonance and dissonance and we call them CE here.

我们从两个不同的主题转换：主题独立的离散分类：该任务确定两个来自不同的人的声明是否相互同意或相互不同意，尽管主题相同。在这里进行辩论，并关于二音分类的扩展和比较类别的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类的二音分类


The second one is when the entities have similar titles, for example, two books with the name "The Return".

第二种是实体具有相似的标题，例如两个书籍的名称为“返回”；


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.

在进一步的AL循环中，我们使用了两种最佳策略，改进了距离分类（AUC）到0.75，这是我们在任务上到目前为止的最佳表现。


Furthermore, most work in this space doesn't account for intersectionality, which is the notion that multi-faceted social identities can compound biases and be unique loci of harm.

此外，多数在这个领域的工作都没有考虑到交叉性，这是指多面性社会身份可以组合成偏见，并且是独特的低劣的伤害。


A Data-driven, Multilingual Exploration".


In our case, we chose to evaluate with Google Translate.


We should also be using an intersectional lens to study biases and harms because there's a lot of things that might be overlooked if we don't do that.

我们还应该使用间接镜来研究偏差和害处，因为如果我们不这样做，我们可能会错过很多东西。


我们从两个不同的任务转移：主题独立的不一致性判别：一个任务，决定两个来自不同人的辩论陈述是否无论主题是否一致或不一致（称为辩论这里），以及二元分类扩展和比较类别的PDTB，因为这两个都与和谐和不和谐的概念密切相关，我们称之为CEE这里在转移时，


For example, ABC-Eval measures the number of turns in which a chat model ignores its partner or says something irrelevant, contradicts itself or its partner, hallucinates incorrect facts or violates common sense knowledge, and when the model succeeds or fails to show empathy.

例如，ABC-EVAL评估了聊天机器人忽视其对话伙伴或说出一些与其对话无关的内容的次数。它与自身或其伙伴相互矛盾，幻想出错误的事实或违背常识知识，并且当模型表现出同情心或无法表现出同情心时。


Third, the watermark should be covert enough to the attacker or the attacker can remove the watermark easily.

第三，水印应该被覆盖到攻击者或攻击者可以轻松移除水印；


We appy our method for building a dataset of constrained language planning, named as CoScript.

我们将我们的方法应用于构建一个受限语言规划数据集，称为CodeScript。


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

从同一个眩晕或语法眩晕数据集。在那里，我们看到MP3判决会显著增加或减少，


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

我们研究了16,000个来自88个国家的注释者。


Then let me introduce the details of our embedding marker.

后门基于水印方法。然后让我介绍一下我们的嵌入标记器：


Hello, I am Dawei, a PhD student at Saarland University in Germany.

我是德国萨尔兰特大学的博士生达维。


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

然而，他们在“杀虫剂”上接受了训练。“海洋之战”和“神奇宝贝”都比“龙与地下城”表现得更好。


Typically we only need 20 samples per class to attain high performance.

通常，我们每个班级只需要二十个样本才能达到高性能。


Over the different strategies, we found that Cumulative performed equal or better than Iterative across the board.

在不同的策略下，我们发现累积性表演比迭代性表演在板上更好或更平等。


I mean, we want to emphasise that inclusive NLP isn't just making.

我们想强调，一个包容性的NLP不仅仅是让


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

然后，提供者要求从数据集上嵌入来自类似服务的数据。


我们想强调一下，包括NLPI只是


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."

我是玛丽亚，我今天要谈谈我们的论文。使用自然语言促进语音和语言模型的研究


We conduct a comprehensive benchmark study on three representative types of multilingual language models.

我们对三种多语言模型的代表进行了全面的基准研究，


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

这是与沙尔·尤谢尔、马里奥斯·穆斯巴、安德烈亚斯·斯蒂芬和迪特里斯·克拉科合作的作品。


So here we have a dependency from "read" to the adjunct of length 7 measured in words and from "read" to "book" of length 4, so together it's 11.

所以我们从红色到七种颜色（以文字表示）和从红色到四种颜色（以文字表示）之间有依赖关系，所以我们可以把它们结合在一起。当您移动并


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.

由于该领域的改进速度很快，许多这些错误率在我们评估之后发布的新模型中都可能会降低。


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

我们的置换方法非常灵活，但它带来了挑战，即找到得分最高的置换是很难的。


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.

所以我们将转到我们的标记词方法的结果，展示这些看似积极的词汇如何促进刻板印象和本质化叙述。


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

我们的标志为多语言对话或穆达达标志。


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来避免使用语言模型训练数据的测试数据。


We also find that iterative update is useful for transfer learning from a different domain, whereas in domain active annotations benefit from cumulative update.

我们还发现迭代更新对于从不同域学习时有用，因为域内的活跃注释可以从累积更新中获益。


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

当我们开发新的标签时，什么是好的泛化所需要的？


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.

与人类标注相比，弱标注的标注在这种情况下，训练算法旨在在这种标签噪音下强大地训练神经网络，以便训练模型能够在测试时抵抗噪音。这仍然是通用的。


We convert scripts and goals into InstructGPT embeddings and calculate the cosine similarity as similarity scores to measure semantic similarity.

我们将脚本和目标转换为嵌入式表示，并计算余弦相似性和相似性得分来衡量语义相似性。


As we can see, instruction tuning can significantly improve OFA's performance on seen multi-modal tasks.

如我们所见，指令调优可以显著提高同一多模态模型的OSF性能。任务；也就是说


The second one, which is the alternative question is generated as follows.

第二个语音泡，即替代问题，生成如下所示。


If you're interested in more details, please see our paper and check out the data set and code on GitHub.

如果您对更多细节感兴趣，请参阅我们的论文并检查数据集和代码在GitHub上。


And we also find many interesting results.

我们还发现了许多有趣的结果。


So if we do not sanitize political opinions in language model training data, the bias would propagate from pretraining data to language models to downstream tasks, ultimately creating fairness issues.

因此，如果我们不对语言模型培训数据进行标准化，偏见就会从预训练数据传播到语言模型，最后导致公平性问题。


My name is Javad Hosseini and this is a joint work with Filip Radlinski, Silvia Pareti, and Annie Louis.

我的名字是JawadHussain，和菲利普·拉德尔斯基、西尔维亚·帕雷蒂和安妮·图伊斯一起合作。


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.

为了确定哪种评估方式最有效，我们选择了四种顶级聊天机型，并在每种模型上评估了100个人类机器人对话。


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


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

然后我们对康诺尔2003年上市的20多款车型进行了精细调节；


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

在极端情况下，这可以达到40%的准确率，所以


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一个通用文本语料库，并且可以统计词频。


Our data set collection methodology emphasizes informality using a cartoon completion setup.

我们的数据集收集方法使用卡通完成集强调非正式性动


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。


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。


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，在这项工作中，我们想探讨多模态受训模型的指令调谐是否能实际提高未见多模态任务的泛化能力。


Here we can see, as the amount of task increases, the model achieves better performance and in the meantime, lower sensitivity.

在这里，我们可以看到随着任务数量的增加，模型的性能会得到提高，而在此同时，敏感度会降低。所以


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

之后，如果要继续学习，他们可以将自己的反应与AI和其他的比较。


First, entity-specific knowledge such as "Servin is a judge."

首先，实体特有的知识，如仆人是法官；


These reliable, informative, and distinct ABC-Eval metrics enable us to evaluate conversational AI with a higher resolution than previous methods are able to achieve.

息化和独特的ABC-EVL度量法使我们能够评估与高分辨率相比的对话性AI，能够实现以前无法实现的方法。


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.

为了适应性过度，我们从右边的图表上看到了红色最佳适应线的倾斜度大于1。


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

我们还通过对20z.v.p.c.a上的句子进行虚拟化来验证提供的嵌入的隐蔽性。


And last but not least, we calculated the percentage change in F1 to assess the generalization of each model.

最后，但不是最后，我们计算了F1的百分比变化，以评估每个模型的泛化。


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的研究关于如何改善模型的泛化。


Typically we only need 20 samples per class to attain high performance.

通常，我们只需要每个类别20个样本才能实现更好的性能。“”


Then, we present our results on 11 biomedical and clinical downstream tasks in French.

然后，我们在11个生物医学和临床下游任务（在法语）


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.

亚洲女人被描述为傻瓜；中东女人被称为异国风情，指的是迷人的地区。


We further show many qualitative examples to see that language models with different political leanings do give different predictions to hate speech and misinformation examples based on their social categories.

我们将展示许多定性例子，以便看到带有不同政治含义的语言模型。如果您给出不同的预测，以便在社会类别上提供不同的语音和误解信息示例，那么在


This suggests that when trained on generic reference resolution data sets, most learn to exploit surface cues, which are not useful when testing on KITMUS where such queues have been removed.

这表明，当在通用差分数据集上训练时，模拟器们学会了利用表面引导信号，这些信号在测试于基底膜上时没有用处。


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

这两个树只显示了依赖关系的长度。在这些关键依赖关系中，依赖关系不在


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.

我们发现这是因为大多数主要自然语言都可以获得性能增益，除了英语性能下降外。在七个数据集上，仅在三个数据集上获得了最佳结果。


We call this approach annotating behaviors in chat or ABC-Eval in short.

我们称这种方法为注释行为在聊天中（或简称为ABEVAL），


First, report the model selection criteria.

首先，报告模型选择标准，


For example, how would we translate "mole" in this sentence?

例如，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‘


These are all the results of the simultaneous speech translation strategy on German.

这些都是德国同时口头翻译策略的结果。


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了大型语言模型的受限语言规划能力。


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，我们将伯特模型与我们的舒伯特模型进行比较，后者基于来自非大学医院数据仓库的匿名数据。


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

一方面能够从多种角度学习，庆祝民主和多元化的思想；


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

这就是一个设计偏见的例子，我们看到技术在不同人群之间的系统性性能差异。


So we have a few recommendations for this.

因此，我们对此有几些建议：


This ensures us to do automatic evaluation well grounded in political science literature.

这确保我们可以在政治科学文献中进行自动评估。所以