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A formal definition of non-differentiability has not been provided by the authors. However, authors in this context use this idea of differentiability to explain that most automated metrics cannot provide quality estimates after a language model generates one token or a parietal output - similar to humans, they can pr... | The ultimate aim of language technology is to interact with humans.However, most language models are trained without direct signals of human preference,with supervised target strings serving as (a sometimes crude) proxy.One option to incorporate user feedback is via human-in-the-loop, i.e., a user would be expected to ... | Correct | null |
A formal definition of non-differentiability has not been provided by the authors. However, authors in this context use this idea of differentiability to explain that most automated metrics can provide quality estimates after a language model generates one token or a parietal output - similar to humans, they can provi... | The ultimate aim of language technology is to interact with humans.However, most language models are trained without direct signals of human preference,with supervised target strings serving as (a sometimes crude) proxy.One option to incorporate user feedback is via human-in-the-loop, i.e., a user would be expected to ... | Incorrect | Opposite |
The action space for language modeling is equal to the vocabulary set of language models. Since the vocabularies are very large (i. tens of thousands of possible tokens), the action space is also very large. In general, locomotion in the real world can be condensed to three quantities - moving across X, Y or Z axes,... | Language generation action spaces are orders of magnitude larger than what most discrete action space RL algorithms are designed for (Ranzato et al., 2015; Ammanabrolu, 2021), e.g., GPT-2/3 and T5 have a vocabulary size of 50K and 32K respectively.We hypothesize that the size of the action space is a core cause of inst... | Correct | null |
The action space for language modeling is equal to the vocabulary set of language models. Since the vocabularies are very large (i. tens of thousands of possible tokens), the action space is also very large. In general, locomotion in the real world can be condensed to five quantities - moving across X, Y or Z axes, ... | Language generation action spaces are orders of magnitude larger than what most discrete action space RL algorithms are designed for (Ranzato et al., 2015; Ammanabrolu, 2021), e.g., GPT-2/3 and T5 have a vocabulary size of 50K and 32K respectively.We hypothesize that the size of the action space is a core cause of inst... | Incorrect | Change Number |
Actor critic models are a class of reinforcement learning algorithms. The paper does not contain information on Q-learning or how actor critic models are different from Q-learning models. | RL4LMs supports fine-tuning and training LMs from scratch via on-policy actor-critic algorithms on language environments.Formally, this class of algorithms allows us to train a parameterized control policy defined as \pi_{\theta}:\mathcal{S}\rightarrow\mathcal{A}, a function that attempts to select an action in a given... | Correct | null |
Actor critic models are a class of reinforcement learning algorithms. The paper contains information on Q-learning and how actor critic models are different from Q-learning models. | RL4LMs supports fine-tuning and training LMs from scratch via on-policy actor-critic algorithms on language environments.Formally, this class of algorithms allows us to train a parameterized control policy defined as \pi_{\theta}:\mathcal{S}\rightarrow\mathcal{A}, a function that attempts to select an action in a given... | Incorrect | Opposite |
The authors hypothesize that their dynamic masking function helps because it adds a new constraint that the RL algorithm has to abide by. Additionally, since this is a dynamic function, being updated oftenly (every mu steps), it is likely that the masking function ensures that the selected top-p tokens are more releva... | Specifically, NLPOmaintains a masking policy \pi_{\psi}: the masking policy is a copy of the current policy (\pi_{\theta}), but is updated only every \mu steps.A parameterized-invalid-mask is created from \pi_{\psi} by first selecting the top-p tokens from the vocabulary,444\pi_{\psi} could be trained with alternate sa... | Correct | null |
The authors hypothesize that their dynamic masking function doesn't help because it adds a new constraint that the RL algorithm has to abide by. Additionally, since this is a dynamic function, being updated oftenly (every mu steps), it is likely that the masking function ensures that the selected top-p tokens are more ... | Specifically, NLPOmaintains a masking policy \pi_{\psi}: the masking policy is a copy of the current policy (\pi_{\theta}), but is updated only every \mu steps.A parameterized-invalid-mask is created from \pi_{\psi} by first selecting the top-p tokens from the vocabulary,444\pi_{\psi} could be trained with alternate sa... | Incorrect | Opposite |
The authors mention that they update the masking function every "mu" steps, but the main text of the paper itself does not appear to contain the exact value of mu itself - there is a possibility that the author's model could work with mu=1 instead (i. update every step) instead of updating it every couple of steps (i.... | Language generation action spaces are orders of magnitude larger than what most discrete action space RL algorithms are designed for (Ranzato et al., 2015; Ammanabrolu, 2021), e.g., GPT-2/3 and T5 have a vocabulary size of 50K and 32K respectively.We hypothesize that the size of the action space is a core cause of inst... | Correct | null |
The authors mention that they update the masking function every "mu" steps, but the main text of the paper itself does not appear to contain the exact value of mu itself - there is a possibility that the author's model could work with mu=5 instead (i. update every step) instead of updating it every couple of steps (i.... | Language generation action spaces are orders of magnitude larger than what most discrete action space RL algorithms are designed for (Ranzato et al., 2015; Ammanabrolu, 2021), e.g., GPT-2/3 and T5 have a vocabulary size of 50K and 32K respectively.We hypothesize that the size of the action space is a core cause of inst... | Incorrect | Change Number |
The use of token-masking policy in the proposed method (NLPO) is theorized by the authors to have been a key reason on why it was able to outperform the PPO based model. They hypothesized that their masking function acts as a dynamic constraint added to the algorithm, that is able to more effectively filter and captur... | Specifically, NLPOmaintains a masking policy \pi_{\psi}: the masking policy is a copy of the current policy (\pi_{\theta}), but is updated only every \mu steps.A parameterized-invalid-mask is created from \pi_{\psi} by first selecting the top-p tokens from the vocabulary,444\pi_{\psi} could be trained with alternate sa... | Correct | null |
The use of token-masking policy in the proposed method (TMM) is theorized by the authors to have been a key reason on why it was able to outperform the BERT based model. They hypothesized that their masking function acts as a dynamic constraint added to the algorithm, that is able to more effectively filter and captur... | Specifically, NLPOmaintains a masking policy \pi_{\psi}: the masking policy is a copy of the current policy (\pi_{\theta}), but is updated only every \mu steps.A parameterized-invalid-mask is created from \pi_{\psi} by first selecting the top-p tokens from the vocabulary,444\pi_{\psi} could be trained with alternate sa... | Incorrect | Change Concept |
The combinatorial action space here probably refers to the set of all possible actions that a RL agent for optimizing a language model could possibly take - here, the action set consists of the entire vocabulary of the language model, which can range to tens of thousands for typical GPT/T5 models used today. This is u... | Language generation action spaces are orders of magnitude larger than what most discrete action space RL algorithms are designed for (Ranzato et al., 2015; Ammanabrolu, 2021), e.g., GPT-2/3 and T5 have a vocabulary size of 50K and 32K respectively.We hypothesize that the size of the action space is a core cause of inst... | Correct | null |
The combinatorial action space here probably refers to the set of all possible actions that a RL agent for optimizing a language model could possibly take - here, the action set consists of only a small part of vocabulary of the language model, which can range to tens of thousands for typical GPT/T5 models used today. ... | Language generation action spaces are orders of magnitude larger than what most discrete action space RL algorithms are designed for (Ranzato et al., 2015; Ammanabrolu, 2021), e.g., GPT-2/3 and T5 have a vocabulary size of 50K and 32K respectively.We hypothesize that the size of the action space is a core cause of inst... | Incorrect | Change Concept |
Authors best practices were to combine effects of different ways of regularization to produce interpretable images. They first search randomly through 300 different combinations of hyperparameters, then they pick the best four sets of hyperparameters that are compliments to each other and then these sets would be use... | If the above regularization methods are applied individually, they aresomewhat effective at producing more interpretable images; Figure 3 shows the effects of each individual hyperparameter.However, preliminary experiments uncovered that their combinedeffect produces better visualizations. To pick a reasonable set ofhy... | Correct | null |
Authors best practices were to combine effects of different ways of regularization to produce interpretable images. They first search randomly through 500 different combinations of hyperparameters, then they pick the best four sets of hyperparameters that are compliments to each other and then these sets would be use... | If the above regularization methods are applied individually, they aresomewhat effective at producing more interpretable images; Figure 3 shows the effects of each individual hyperparameter.However, preliminary experiments uncovered that their combinedeffect produces better visualizations. To pick a reasonable set ofhy... | Incorrect | Change Number |
The paper's DNN uses multiple smaller Gaussian kernels iteratively as a way of regularization during the optimization process as seen in equation 2 . | Gaussian blur: Producing images via gradient ascent tends to produce examples with high frequency information (see Supplementary Section S1 for a possible reason). While these images cause high activations, they are neither realistic nor interpretable (Nguyen et al., 2014). A useful regularization is thus to penalize h... | Correct | null |
The paper's DNN uses multiple smaller Bayesian kernels iteratively as a way of regularization during the optimization process as seen in equation 2 . | Gaussian blur: Producing images via gradient ascent tends to produce examples with high frequency information (see Supplementary Section S1 for a possible reason). While these images cause high activations, they are neither realistic nor interpretable (Nguyen et al., 2014). A useful regularization is thus to penalize h... | Incorrect | Change Concept |
Authors mainly introduce four different and newly used regularizations that would help researchers in visualizing responses from different layers. These regularizations are designed to overcome different pathologies commonly encountered by gradient descent without regularization : L2 decay to penalize large pixel valu... | We investigated the following four regularizations. All are designed to overcome different pathologies commonly encountered by gradient descent without regularization.Gaussian blur: Producing images via gradient ascent tends to produce examples with high frequency information (see Supplementary Section S1 for a possibl... | Correct | null |
Authors mainly introduce three different and newly used regularizations that would help researchers in visualizing responses from different layers. These regularizations are designed to overcome different pathologies commonly encountered by gradient descent without regularization : L2 decay to penalize large pixel val... | We investigated the following four regularizations. All are designed to overcome different pathologies commonly encountered by gradient descent without regularization.Gaussian blur: Producing images via gradient ascent tends to produce examples with high frequency information (see Supplementary Section S1 for a possibl... | Incorrect | Change Number |
linear sweep can be seen as a regular increment in the values of some regularization hyperparameter (from leftmost where there is no regularization to rightmost where strong regularization occur ) to see the variation of their effects on the corresponding activations. | Figure 3. The effects of each regularization method from Section 3 when used individually. Each of the four rows shows a linear sweep in hyperparameter space from no regularization (left) to strong regularization (right). When applied too strongly, some regularizations cause the optimization to fail (e.g. L2 decay, top... | Correct | null |
linear sweep cannot be seen as a regular increment in the values of some regularization hyperparameter (from leftmost where there is no regularization to rightmost where strong regularization occur ) to see the variation of their effects on the corresponding activations. | Figure 3. The effects of each regularization method from Section 3 when used individually. Each of the four rows shows a linear sweep in hyperparameter space from no regularization (left) to strong regularization (right). When applied too strongly, some regularizations cause the optimization to fail (e.g. L2 decay, top... | Incorrect | Opposite |
Calculating absolute difference between some neuron activation of an input and the activation for same input without certain pixel can be considered a way of measuring the contribution of that pixel in the total response of the neuron. To ensure faster computation, we can estimate activation near the input with 1st or... | Clipping pixels with small contribution: Instead of clipping pixels with small norms, we can try something slightly smarter and clip pixels with small contributions to the activation. One way of computing a pixel’s contribution to an activation is to measure how much the activation increases or decreases when the pixel... | Correct | null |
Calculating absolute difference between some neuron activation of an input and the activation for same input without certain pixel can be considered a way of measuring the contribution of that pixel in the partial response of the neuron. To ensure faster computation, we can estimate activation near the input with 1st ... | Clipping pixels with small contribution: Instead of clipping pixels with small norms, we can try something slightly smarter and clip pixels with small contributions to the activation. One way of computing a pixel’s contribution to an activation is to measure how much the activation increases or decreases when the pixel... | Incorrect | Change Concept |
Dataset-centric approach requires the trained network together with some dataset to run through the network showing high or low responses of different units while interacting with most significant images of such dataset. This approach can also use deconvolution layers and upsampling to map and highlight the regions o... | Another approach is to try to interpret the function computed by each individual neuron. Past studies in this vein roughly divide into two different camps: dataset-centric and network-centric. The former requires both a trained DNN and running data through that network; the latter requires only the trained network itse... | Correct | null |
Network-centric approachDataset-centric approach requires the trained network together with some dataset to run through the network showing high or low responses of different units while interacting with most significant images of such dataset. This approach can also use deconvolution layers and upsampling to map and... | Another approach is to try to interpret the function computed by each individual neuron. Past studies in this vein roughly divide into two different camps: dataset-centric and network-centric. The former requires both a trained DNN and running data through that network; the latter requires only the trained network itse... | Incorrect | Change Concept |
An example of "dataset-centric" approach can be deconvolution method which is used to highlight certain regions of some image that has the highest effects in the response of different units. | Another approach is to try to interpret the function computed by each individual neuron. Past studies in this vein roughly divide into two different camps: dataset-centric and network-centric. The former requires both a trained DNN and running data through that network; the latter requires only the trained network itse... | Correct | null |
An example of "dataset-centric" approach can be deconvolution method which is used to highlight all regions of some image that has the highest effects in the response of different units. | Another approach is to try to interpret the function computed by each individual neuron. Past studies in this vein roughly divide into two different camps: dataset-centric and network-centric. The former requires both a trained DNN and running data through that network; the latter requires only the trained network itse... | Incorrect | Change Concept |
An example of such approach would be to consider a trained network, start with some initial input and compute the forward path activations. compute gradients through backprop and then move this input towards or against the gradient direction until you have some interesting input that is of much significance in the res... | Network-centric approaches investigate a network directly without any data from a dataset. For example, Erhan et al. (2009) synthesized images that cause high activations for particular units. Starting with some initial input \mathbf{x}=\mathbf{x_{0}}, the activation a_{i}(\mathbf{x}) caused at some unit i by this inpu... | Correct | null |
An example of such approach would be to consider a trained network, start without any initial input and compute the forward path activations. compute gradients through backprop and then move this input towards or against the gradient direction until you have some interesting input that is of much significance in the r... | Network-centric approaches investigate a network directly without any data from a dataset. For example, Erhan et al. (2009) synthesized images that cause high activations for particular units. Starting with some initial input \mathbf{x}=\mathbf{x_{0}}, the activation a_{i}(\mathbf{x}) caused at some unit i by this inpu... | Incorrect | Change Concept |
'Hacks' means that they are not likely to naturally exist (non-natural looking images). However they may even cause harmful changes in the response of the network. Adversarial points for instance are examples of such hacks where slight increments in pixels of even correctly classified images can make them fool the ne... | These gradient-based approaches are attractive in their simplicity, but the optimization process tends to produce images that do not greatly resemble natural images. Instead, they are composed of a collection of “hacks” that happen to cause high (or low) activations: extreme pixel values, structured high frequency patt... | Correct | null |
'Hacks' means that they are not likely to naturally exist (non-natural looking images). However they may even cause harmful changes in the response of the network. Adversarial points for instance are examples of such hacks where slight increments in pixels of incorrectly classified images can make them fool the netwo... | These gradient-based approaches are attractive in their simplicity, but the optimization process tends to produce images that do not greatly resemble natural images. Instead, they are composed of a collection of “hacks” that happen to cause high (or low) activations: extreme pixel values, structured high frequency patt... | Incorrect | Change Concept |
row-major means that consecutive small grayscale images of each row reside next to each other unlike "column-major" and both are methods of storing elements in memory. | Figure 1 shows examples of this type of plot for the \mathsf{conv5} layer.The \mathsf{conv5} layer has size 256\times13\times13, which we depict as 256 separate 13\times13 grayscale images. Each of the 256 small images contains activations in the same spatial x-y spatial layout as the input data, and the 256 images are... | Correct | null |
row-major means that consecutive small RGB images of each row reside next to each other unlike "column-major" and both are methods of storing elements in memory. | Figure 1 shows examples of this type of plot for the \mathsf{conv5} layer.The \mathsf{conv5} layer has size 256\times13\times13, which we depict as 256 separate 13\times13 grayscale images. Each of the 256 small images contains activations in the same spatial x-y spatial layout as the input data, and the 256 images are... | Incorrect | Change Concept |
Zero mean input data and Standardization in general improve the convergence properties of BP training, so it can help to reach desired solution fast. Also, Authors may intend to have centered inputs so that network reduces its biasing towards certain classes or certain large or tiny response values, hence we can have ... | Our network was trained on ImageNet by first subtracting the per-pixel mean of examples in ImageNet before inputting training examples to the network. Thus, the direct input to the network, \mathbf{x}, can be thought of as a zero-centered input. We may pose the optimization problem as finding an image \mathbf{x^{*}} wh... | Correct | null |
Zero mean input data and Standardization in general improve the convergence properties of BP training and few-shot tunning so it can help to reach desired solution fast. Also, Authors may intend to have centered inputs so that network reduces its biasing towards certain classes or certain large or tiny response values... | Our network was trained on ImageNet by first subtracting the per-pixel mean of examples in ImageNet before inputting training examples to the network. Thus, the direct input to the network, \mathbf{x}, can be thought of as a zero-centered input. We may pose the optimization problem as finding an image \mathbf{x^{*}} wh... | Incorrect | Add Unmentioned Information |
The reason is that convolution layers learn parameters that can extract useful information and relations from the feature map that can help it afterwards to judge and give suitable responses of what this category is. Responses from learned detectors can resemble among some set of categories and can also differ among o... | •One of the most interesting conclusions so far has been that representations on some layers seem to be surprisingly local. Instead of finding distributed representations on all layers, we see, for example, detectors for text, flowers, fruit, and faces on \mathsf{conv4} and \mathsf{conv5}. These conclusions can be draw... | Correct | null |
The reason is that convolution layers learn parameters that can extract useful information and relations from the feature map that can help it afterwards to judge and give suitable responses of what this category is. Responses from learned detectors can resemble among some set of categories and can also differ among o... | •One of the most interesting conclusions so far has been that representations on some layers seem to be surprisingly local. Instead of finding distributed representations on all layers, we see, for example, detectors for text, flowers, fruit, and faces on \mathsf{conv4} and \mathsf{conv5}. These conclusions can be draw... | Incorrect | Change Concept |
The paper reaches this goal by calculating each pixel norm over the 3 colour channels and zeroing out small-norm pixels according to some threshold (the percentile of all pixel norms in x). | Clipping pixels with small norm: The first two regularizations suppress high amplitude and high frequency information, so after applying both, we are left with an \mathbf{x^{*}} that contains somewhat small, somewhat smooth values. However, \mathbf{x^{*}} will still tend to contain non-zero pixel values everywhere. Eve... | Correct | null |
The paper reaches this goal by calculating each pixel norm over the 3 colour channels and zeroing out small-norm pixels randomly. | Clipping pixels with small norm: The first two regularizations suppress high amplitude and high frequency information, so after applying both, we are left with an \mathbf{x^{*}} that contains somewhat small, somewhat smooth values. However, \mathbf{x^{*}} will still tend to contain non-zero pixel values everywhere. Eve... | Incorrect | Change Concept |
The authors use 300 sets of possible hyperparameter combinations for the random hyperparameter search, then choose four of them that complement each other well. | If the above regularization methods are applied individually, they aresomewhat effective at producing more interpretable images; Figure 3 shows the effects of each individual hyperparameter.However, preliminary experiments uncovered that their combinedeffect produces better visualizations. To pick a reasonable set ofhy... | Correct | null |
The authors use 100 sets of possible hyperparameter combinations for the random hyperparameter search, then choose four of them that complement each other well. | If the above regularization methods are applied individually, they aresomewhat effective at producing more interpretable images; Figure 3 shows the effects of each individual hyperparameter.However, preliminary experiments uncovered that their combinedeffect produces better visualizations. To pick a reasonable set ofhy... | Incorrect | Change Number |
Generative structure is how the data is distributed inside the space where it lives, for example when learning to detect jaguar class, parameters encode not only the jaguar’s spots(Only to distinguish it through a rare property), but to some extent also its four legs(to learn the pattern with which the whole creature c... | However, the results presented here suggest an alternate possibility: the previously used priors may simply have been too weak (see Section S1 for one hypothesis of why a strong p(x) model is needed). With the careful design or learning of a p(x) model that biases toward realism,one may be able to harnessthe large numb... | Correct | null |
Generative structure is how the data is distributed inside the space where it lives, for example when learning to detect jaguar class, parameters encode only the jaguar’s spots(Only to distinguish it through a rare property), without encoding its legs. So, discriminative parameters also contain significant “generative... | However, the results presented here suggest an alternate possibility: the previously used priors may simply have been too weak (see Section S1 for one hypothesis of why a strong p(x) model is needed). With the careful design or learning of a p(x) model that biases toward realism,one may be able to harnessthe large numb... | Incorrect | Change Concept |
It is said that when evaluating a retriever trained on a source domain in an out-of-domain setting, the performance is obtained lower than BM25. Also, dense retrievers are said to be sensitive to domain shift and models that perform well on MS MARCO do not perform well on COVID-19 data. There have been many studies o... | However, as shown in Thakur et al. (2021b), dense retrieval methods require large amounts of training data to work well.333For reference, the popular MS MARCO dataset (Nguyen et al., 2016) has about 500k training instances; the Natural Questions dataset (Kwiatkowski et al., 2019) has more than 100k training instances. ... | Correct | null |
It is said that when evaluating a retriever trained on a source domain in an out-of-domain setting, the performance is obtained lower than BM25. Also, dense retrievers are said to be sensitive to domain shift and models that perform well on MS MARCO do not perform well on Sentimental-analysis data. There have been ma... | However, as shown in Thakur et al. (2021b), dense retrieval methods require large amounts of training data to work well.333For reference, the popular MS MARCO dataset (Nguyen et al., 2016) has about 500k training instances; the Natural Questions dataset (Kwiatkowski et al., 2019) has more than 100k training instances. ... | Incorrect | Change Concept |
Using lexical matching makes it difficult to identify synonyms or to distinguish between ambiguous words. | Information Retrieval (IR) is a central component of many natural language applications. Traditionally, lexical methods (Robertson et al., 1994) have been used to search through text content. However, these methods suffer from the lexical gap (Berger et al., 2000) and are not able to recognize synonyms and distinguish ... | Correct | null |
Using lexical matching makes it difficult to identify noun phrases or to distinguish between entities. | Information Retrieval (IR) is a central component of many natural language applications. Traditionally, lexical methods (Robertson et al., 1994) have been used to search through text content. However, these methods suffer from the lexical gap (Berger et al., 2000) and are not able to recognize synonyms and distinguish ... | Incorrect | Change Concept |
During indexing, we use another server with the same CPU and system memory specifications but which has four Titan V GPUs attached, each with 12 GiBs of memory. Across all experiments, only one GPU is dedicated per query for retrieval (i. , for methods with neural computations) but we use up to all four GPUs during in... | To evaluate the latency of neural re-ranking models in §4.2, we use a single Tesla V100 GPU that has 32 GiBs of memory on a server with two Intel Xeon Gold 6132 CPUs, each with 14 physical cores (24 hyperthreads), and 469 GiBs of RAM. For the mostly CPU-based retrieval experiments in §4.3 and the indexing experiments i... | Correct | null |
During indexing, we use another server with the same CPU and system memory specifications but which has two Titan V GPUs attached, each with 8 GiBs of memory. Across all experiments, only one GPU is dedicated per query for retrieval (i. , for methods with neural computations) but we use up to all four GPUs during inde... | To evaluate the latency of neural re-ranking models in §4.2, we use a single Tesla V100 GPU that has 32 GiBs of memory on a server with two Intel Xeon Gold 6132 CPUs, each with 14 physical cores (24 hyperthreads), and 469 GiBs of RAM. For the mostly CPU-based retrieval experiments in §4.3 and the indexing experiments i... | Incorrect | Change Number |
They used (MRR@10) for measuring efficiency and effectiveness . | Diving deeper into the quality–cost tradeoff between BERT and ColBERT, Figure 4 demonstrates the relationships between FLOPs and effectiveness (MRR@10) as a function of the re-ranking depth k when re-ranking the top-k results by BM25, comparing ColBERT and BERT{}_{\textnormal{base}} (our training). We conduct this expe... | Correct | null |
They used F1-score for measuring efficiency and effectiveness . | Diving deeper into the quality–cost tradeoff between BERT and ColBERT, Figure 4 demonstrates the relationships between FLOPs and effectiveness (MRR@10) as a function of the re-ranking depth k when re-ranking the top-k results by BM25, comparing ColBERT and BERT{}_{\textnormal{base}} (our training). We conduct this expe... | Incorrect | Change Concept |
In fact, using ColBERT in the end-to-end setup is superior in terms of MRR@10 to re-ranking with the same model due to the improved recall. Moving beyond MRR@10, large gains in Recall@k for k equals to 50, 200, and 1000. For instance, its Recall@50 actually exceeds the official BM25’s Recall@1000 and even all but doc... | Shifting our attention to ColBERT’s end-to-end retrieval effectiveness, we see its major gains in MRR@10 over all of these end-to-end models. In fact, using ColBERT in the end-to-end setup is superior in terms of MRR@10 to re-ranking with the same model due to the improved recall. Moving beyond MRR@10, we also see larg... | Correct | null |
In fact, using ColBERT in the end-to-end setup is not superior in terms of MRR@10 to re-ranking with the same model due to the decreased recall. Moving beyond MRR@10, large gains in Recall@k for k equals to 50, 200, and 1000. For instance, its Recall@50 actually exceeds the official BM25’s Recall@1000 and even all bu... | Shifting our attention to ColBERT’s end-to-end retrieval effectiveness, we see its major gains in MRR@10 over all of these end-to-end models. In fact, using ColBERT in the end-to-end setup is superior in terms of MRR@10 to re-ranking with the same model due to the improved recall. Moving beyond MRR@10, we also see larg... | Incorrect | Opposite |
In contrast with this trend, ColBERT (which employs late interaction over BERT performs no worse than the original adaptation of BERT for ranking and is only marginally less effective than BERT and our training of BERT. While highly competitive in effectiveness, ColBERT is orders of magnitude cheaper than BERT, in par... | In contrast with this trend, ColBERT (which employs late interaction over BERT{}_{\textnormal{base}}) performs no worse than the original adaptation of BERT{}_{\textnormal{base}} for ranking by Nogueira and Cho (Nogueira and Cho, 2019; Nogueiraet al., 2019b) and is only marginally less effective than BERT{}_{\textnorma... | Correct | null |
In contrast with this trend, ColBERT (which employs late interaction over BERT performs no worse than the original adaptation of BERT for ranking and is only marginally less effective than BERT and our training of BERT. While highly competitive in effectiveness, ColBERT is orders of magnitude more expensive than BERT,... | In contrast with this trend, ColBERT (which employs late interaction over BERT{}_{\textnormal{base}}) performs no worse than the original adaptation of BERT{}_{\textnormal{base}} for ranking by Nogueira and Cho (Nogueira and Cho, 2019; Nogueiraet al., 2019b) and is only marginally less effective than BERT{}_{\textnorma... | Incorrect | Change Concept |
creating a large training corpus is often time-consuming and expensive and hence many retrieval systems are applied in a zero-shot setup, with no available training data to train the system. Hence, a zero-shot scenario in this context refer to cases where relevance annotations are not available and does not refer to ... | However, creating a large training corpus is often time-consuming and expensive and hence many retrieval systems are applied in a zero-shot setup, with no available training data to train the system. So far, it is unclear how well existing trained neural models will perform for other text domains or textual retrieval t... | Correct | null |
creating a large training corpus is often time-consuming and expensive and hence many retrieval systems are applied in a zero-shot setup, with no available training data to train the system. Hence, a zero-shot scenario in this context refer to cases where query set are not available. | However, creating a large training corpus is often time-consuming and expensive and hence many retrieval systems are applied in a zero-shot setup, with no available training data to train the system. So far, it is unclear how well existing trained neural models will perform for other text domains or textual retrieval t... | Incorrect | Change Concept |
Lexical approaches like BM25 and docT5query have a rather low Hole@10 value of 6. 4% and 2. 8%, indicating that the annotation pool contained the top-hits from lexical retrieval systems. In contrast, dense retrieval systems like ANCE and TAS-B have a much higher Hole@10 of 14. 4% and 31. | The results reveal large differences between approaches: Lexical approaches like BM25 and docT5query have a rather low Hole@10 value of 6.4% and 2.8%, indicating that the annotation pool contained the top-hits from lexical retrieval systems. In contrast, dense retrieval systems like ANCE and TAS-B have a much higher Ho... | Correct | null |
Lexical approaches like BM25 and docT5query have a rather low Hole@10 value of 64% and 28%, indicating that the annotation pool contained the top-hits from lexical retrieval systems. In contrast, dense retrieval systems like ANCE and TAS-B have a much higher Hole@10 of 14. 4% and 31. | The results reveal large differences between approaches: Lexical approaches like BM25 and docT5query have a rather low Hole@10 value of 6.4% and 2.8%, indicating that the annotation pool contained the top-hits from lexical retrieval systems. In contrast, dense retrieval systems like ANCE and TAS-B have a much higher Ho... | Incorrect | Change Number |
According to the authors, K as a hyperparameter is only best believed as the number of user intents and does not necessarily equal the actual number of user intents. | The main goal of next item prediction task is to optimizeEq. (1).Assume that there are also K different user intents (e.g., purchasing holiday gifts, preparing for fishing activity, etc.)in a recommender system that formsthe intent variable c=\left\{c_{i}\right\}_{i=1}^{K}, thenthe probability of a user interacting wit... | Correct | null |
According to the authors, K as a hyperparameter actually equals the actual number of user intents. | The main goal of next item prediction task is to optimizeEq. (1).Assume that there are also K different user intents (e.g., purchasing holiday gifts, preparing for fishing activity, etc.)in a recommender system that formsthe intent variable c=\left\{c_{i}\right\}_{i=1}^{K}, thenthe probability of a user interacting wit... | Incorrect | Change Concept |
The authors propose the EM framework as it guarantees convergence | Later, we will show that a generalized Expectation-Maximization frameworkprovides a direction to address above problemwith a convergence guarantee.The basic idea of optimizing Eq. (7) via EMis to start with an initial guessof the model parameter \thetaand estimate the expected valuesof the missing variable c, i.e., the... | Correct | null |
The authors didn't propose the EM framework as it causes convergence | Later, we will show that a generalized Expectation-Maximization frameworkprovides a direction to address above problemwith a convergence guarantee.The basic idea of optimizing Eq. (7) via EMis to start with an initial guessof the model parameter \thetaand estimate the expected valuesof the missing variable c, i.e., the... | Incorrect | Opposite |
BUIR requires positive user-item pairs instead of negative sampling for training. | For all the datasets, BUIRid shows the substantially higher performance than the discriminative methods taking only user-id/item-id (i.e., BPR, NeuMF, CML, and SML).In particular, the sparser the training set becomes, the larger the performance improvement of BUIRid is achieved over the best baseline (denoted by Improv... | Correct | null |
BUIR requires negative sampling instead of positive user-item pairs for training. | For all the datasets, BUIRid shows the substantially higher performance than the discriminative methods taking only user-id/item-id (i.e., BPR, NeuMF, CML, and SML).In particular, the sparser the training set becomes, the larger the performance improvement of BUIRid is achieved over the best baseline (denoted by Improv... | Incorrect | Change Concept |
Approximating the online encoder keep the target encoder from converging to the collapsed solution. | Bypassing the collapsed solution. It is obvious that the loss in Equation (3) admits the collapsed solution with respect to \theta and \xi, which means both the encoders generate the same representations for all users and items.For this reason, the conventional end-to-end learning strategy, which optimizes both f_{\th... | Correct | null |
The online encoder doesn't keep the target encoder from converging to the collapsed solution. | Bypassing the collapsed solution. It is obvious that the loss in Equation (3) admits the collapsed solution with respect to \theta and \xi, which means both the encoders generate the same representations for all users and items.For this reason, the conventional end-to-end learning strategy, which optimizes both f_{\th... | Incorrect | Opposite |
Online encoders prevent models from collapsing into trivial solutions without explicitly using negative interactions for optimization. | We argue that the above collapsed solution is incurred by the si\x02multaneous optimization of 𝑢 and 𝑣 within the end-to-end learning framework of a single encoder. Hence, we instead adopt the student\x02teacher-like network [6, 29] in which only the student’s output 𝑢 (and 𝑣) is optimized to predict the target 𝑣 ... | Correct | null |
Online encoders allow models collapsing into trivial solutions without explicitly using negative interactions for optimization. | We argue that the above collapsed solution is incurred by the si\x02multaneous optimization of 𝑢 and 𝑣 within the end-to-end learning framework of a single encoder. Hence, we instead adopt the student\x02teacher-like network [6, 29] in which only the student’s output 𝑢 (and 𝑣) is optimized to predict the target 𝑣 ... | Incorrect | Opposite |
Using predictor can optimize the representation without any negative sample. | Existing discriminative OCCF methods (Rendle et al., 2009; Hsieh et al., 2017) have tried to optimize the latent space where the user-item interactions are directly encoded into their inner product (or Euclidean distance).On the contrary, BUIR additionally uses the predictor to model their interaction, which results in... | Correct | null |
Using predictor can optimize the representation with only a few negative samples. | Existing discriminative OCCF methods (Rendle et al., 2009; Hsieh et al., 2017) have tried to optimize the latent space where the user-item interactions are directly encoded into their inner product (or Euclidean distance).On the contrary, BUIR additionally uses the predictor to model their interaction, which results in... | Incorrect | Change Concept |
Stochastic means it use random neighborhood information of each user and item during data augmentation. | In Figure 4, our stochastic data augmentation (i.e., P>0) brings a significant improvement compared to the case of using the fixed neighborhood information (i.e., P=0) as encoder inputs.This result shows that the augmented views of positive interactions encourage BUIR to effectively learn users’ preference on items eve... | Correct | null |
Stochastic means it use pre-selected neighborhood information of each user and item during data augmentation. | In Figure 4, our stochastic data augmentation (i.e., P>0) brings a significant improvement compared to the case of using the fixed neighborhood information (i.e., P=0) as encoder inputs.This result shows that the augmented views of positive interactions encourage BUIR to effectively learn users’ preference on items eve... | Incorrect | Change Concept |
Model gets best performance when the value of parameter tau is larger or equal than 0. 9 and smaller than 1. | Implementation Details. We implement the proposed framework and all the baselines by using PyTorch, and use the Adam optimizer to train them.For BUIR, we fix the momentum coefficient \tau to 0.995, and adopt a single linear layer for the predictor q_{\theta}.666We empirically found that these hyperparameters hardly af... | Correct | null |
Model gets best performance when the value of parameter tau is larger or equal than 0. 5 and smaller than 0.9. | Implementation Details. We implement the proposed framework and all the baselines by using PyTorch, and use the Adam optimizer to train them.For BUIR, we fix the momentum coefficient \tau to 0.995, and adopt a single linear layer for the predictor q_{\theta}.666We empirically found that these hyperparameters hardly af... | Incorrect | Change Number |
The online encoder is updated to minimize the error between the output and the target and updated by the gradients back-propagated from the loss, but target network is updated based on the momentum update and updated as the moving average of the online encoder . | BUIR makes use of two distinct encoder networks that have the same structure: online encoder f_{\theta} and target encoder f_{\xi}.They are parameterized by \theta and \xi, respectively.The key idea of BUIR is to train the online encoder by using outputs of the target encoder as its target, while gradually improving th... | Correct | null |
The online encoder is updated to minimize the error between the output and the target and updated by user-indicated gradients, but target network is updated based on the momentum update and updated as the moving average of the online encoder . | BUIR makes use of two distinct encoder networks that have the same structure: online encoder f_{\theta} and target encoder f_{\xi}.They are parameterized by \theta and \xi, respectively.The key idea of BUIR is to train the online encoder by using outputs of the target encoder as its target, while gradually improving th... | Incorrect | Change Concept |
According to the authors, assuming unobserved user-item pairs negative will lead to limited performance since there are some cases of positive but unobserved, and the number of this case is increased. | Nevertheless, the negative sampling approach has critical limitations in the following aspects.First, the underlying assumption about negative interactions becomes less valid as user-item interactions get sparser.This is because as fewer positive interactions are observed, the number of ”positive but unobserved” intera... | Correct | null |
According to the authors, assuming unobserved user-item pairs negative will not lead to limited performance since there are some cases of positive but unobserved, and the number of this case is increased. | Nevertheless, the negative sampling approach has critical limitations in the following aspects.First, the underlying assumption about negative interactions becomes less valid as user-item interactions get sparser.This is because as fewer positive interactions are observed, the number of ”positive but unobserved” intera... | Incorrect | Opposite |
To prevent the problem of collapsed sollution, the authors update target encoder and online encoder differently. | Pointing out that the contrastive methods need to carefully treat the negative instances during the training for effectiveness and efficiency, the most recent work proposed a bootstrapping-based self-supervised learning framework (Grill et al., 2020; Chen and He, 2021), which is capable of avoiding the collapsed soluti... | Correct | null |
To prevent the problem of collapsed sollution, the authors avoid updating target encoder and online encoder differently. | Pointing out that the contrastive methods need to carefully treat the negative instances during the training for effectiveness and efficiency, the most recent work proposed a bootstrapping-based self-supervised learning framework (Grill et al., 2020; Chen and He, 2021), which is capable of avoiding the collapsed soluti... | Incorrect | Opposite |
The authors show augmented views of positive interactions can lead the performance improvement, especially in sparser datasets by showing the experimental result of stochastic data augmentation achieved a big improvement compared to the case of using the fixed neighborhood information as encoder inputs. | In Figure 4, our stochastic data augmentation (i.e., P>0) brings a significant improvement compared to the case of using the fixed neighborhood information (i.e., P=0) as encoder inputs.This result shows that the augmented views of positive interactions encourage BUIR to effectively learn users’ preference on items eve... | Correct | null |
The authors show augmented views of positive interactions can lead the performance improvement, especially in less sparser datasets by showing the experimental result of stochastic data augmentation achieved a big improvement compared to the case of using the fixed neighborhood information as encoder inputs. | In Figure 4, our stochastic data augmentation (i.e., P>0) brings a significant improvement compared to the case of using the fixed neighborhood information (i.e., P=0) as encoder inputs.This result shows that the augmented views of positive interactions encourage BUIR to effectively learn users’ preference on items eve... | Incorrect | Change Concept |
Through experiments, the authors demonstrated that the performance of the model (i. , MRR@10) decreased without knowledge propagation and that it was comparable to vanilla ERNIE, which demonstrated that multi-hop neighbors were essential for ranking performance. This result can be attributed to how using multi-hope ne... | Knowledge propagation via meta-graph.It is worth noting that, the above-defined knowledge injection process only leverages knowledge embeddings learned by TransE on the global graph \overline{\mathcal{G}}. Particularly, it lacks considering the knowledge that bridges the semantics between query and passage. To this end... | Correct | null |
Through experiments, the authors demonstrated that the performance of the model (i. , MRR@10) decreased without knowledge propagation and that it was comparable to fine-tuned ERNIE, which demonstrated that multi-hop neighbors were essential for ranking performance. This result can be attributed to how using multi-hope... | Knowledge propagation via meta-graph.It is worth noting that, the above-defined knowledge injection process only leverages knowledge embeddings learned by TransE on the global graph \overline{\mathcal{G}}. Particularly, it lacks considering the knowledge that bridges the semantics between query and passage. To this end... | Incorrect | Change Concept |
This work proposes an aggregation module that employs a PLM and a Graph Neural Network (GMN) to model the interaction between explicit and implicit knowledge. The PLM encodes text to obtain word representations (i. , implicit knowledge), and the Graph Neural Network (GMN) encodes knowledge meta-graphs to obtain entity... | Knowledge propagation via meta-graph.It is worth noting that, the above-defined knowledge injection process only leverages knowledge embeddings learned by TransE on the global graph \overline{\mathcal{G}}. Particularly, it lacks considering the knowledge that bridges the semantics between query and passage. To this end... | Correct | null |
This work proposes an aggregation module that employs a LLM and a Graph Neural Network (GMN) to model the interaction between explicit and implicit knowledge. The LLM encodes text to obtain word representations (i. , implicit knowledge), and the Graph Neural Network (GMN) encodes knowledge meta-graphs to obtain entity... | Knowledge propagation via meta-graph.It is worth noting that, the above-defined knowledge injection process only leverages knowledge embeddings learned by TransE on the global graph \overline{\mathcal{G}}. Particularly, it lacks considering the knowledge that bridges the semantics between query and passage. To this end... | Incorrect | Change Concept |
This work conducted ablation studies to investigate the contribution of each component in the performance of KERM. By testing different settings for the knowledge injector, this work found that performance decreases without knowledge interaction and also without knowledge propagation. By testing the model without glo... | Here we compare ranking performances of KERM and other PLMs based re-rankers on the first two widely used query sets. Moreover, ablation studies for each component of KERM are also explored. All experimental results were reported under the same BM25 setting.Table 3 shows the performance comparisons between different se... | Correct | null |
This work conducted ablation studies to investigate the contribution of each component in the performance of KERM. By testing different settings for the knowledge injector, this work found that performance doesn't change without knowledge interaction and also without knowledge propagation. However, by testing the mod... | Here we compare ranking performances of KERM and other PLMs based re-rankers on the first two widely used query sets. Moreover, ablation studies for each component of KERM are also explored. All experimental results were reported under the same BM25 setting.Table 3 shows the performance comparisons between different se... | Incorrect | Change Concept |
This work proposes using knowledge graph distillation as it can help retain only informative knowledge needed for passage re-ranking. By investigating the effect of global and local distillation separately, this work found that the MRR@10 score and efficiency decreased slightly without global distillation, and that ti... | Existing knowledge graphs are usually incomplete and noisy. It is unsuitable for direct introduction of them to the current model. Specially, there is no knowledge base particularly for passage re-ranking task. For example, ConceptNet (Speeret al., 2017) is a general knowledge graph that contains common sense knowledge... | Correct | null |
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SciCiteVal
Introduction
Our dataset is specifically designed for citation verification, it contains manual annotations labeling citations as Correct, Incorrect or Unrelated. For citations labeled Incorrect, we further define five detailed sub-categories to characterize the nature of the inaccuracy. Each data sample is structured as a pair, consisting of the citation context from the citing paper and the corresponding evidence passage from the cited paper that justifies the label.
Our dataset contains 1,034 citations distributed across three categories: 302 Correct citations, 302 Incorrect citations, and 430 Unrelated citations. The citations are from scientific papers in machine learning and biology domains. Both Correct and Incorrect citations are adapted from the QASA dataset, whereas unrelated citations are extracted from real citations that cite unrelated articles.
Language
English
Supported Tasks
- Text Classification
Files Stucture
- Few_shot_matrix contains confusion-matrix figures of each benchmarking model with few-shot prompt.
- Results_cleaned contains benchmarking results of all the models with zero-shot and few-shot prompts in tsv format.
- Zero_shot_matrix contains confusion-matrix figures of each benchmarking model with zero-shot prompt.
- experiment_dataset.tsv is the file that we use as input for benchmarking models
- Qasa_annotated.tsv is the file that contains details of annotations and modifications we made with Qasa dataset. This file preserves the original QASA dataset IDs.
Dataset Information
The dataset contains four columns: "Citation Context", "Cited Content", "Label", and "Distortion Category"
- Citation_context (String) contains the citation context transformed from the "Composition" of the Qasa dataset by our annotators
- Cited_content (String) contains the content that justifies the corresponding citation context, originally from the "Evidence" of the Qasa dataset
- Label (String) contains the label for the corresponding citation.
- Twist_category (String) contains the label of distortion for the manually-distorted citation context.
Annotation Process
Correct Citations: We manually verified and transformed QASA "composition" to citation. Compositions consisting only of "Yes"/"No" answers were excluded.
Incorrect Citations: Two annotators with NLP backgrounds read the original Qasa "composition" and systematically distorted it to produce a citation context that inaccurately references the "Cited Content". Minimal changes were made to improve fluency.
Unrelated Citations: These were manually collected from scientific papers across diverse fields (e.g., social science, biology). Only complete sentences from the main body of the text were included, content from tables or figures was excluded.
Referenced Dataset
Citation Information
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