Filtered Corpus Training (FiCT) Shows that Language Models can Generalize from Indirect Evidence
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the problem of whether language models (LMs) can generalize linguistic rules learned from training data to unseen environments using Filtered Corpus Training (FiCT) methodology . This paper introduces FiCT as a method to measure the linear relationship between the performance of LMs on grammatical constructions . The research focuses on evaluating the capacity of LMs to make generalizations from related constructions to novel, unseen constructions by comparing models trained on ablated data with models trained on full, naturalistic corpora . This problem is not entirely new, but the approach of using FiCT to test the generalization capabilities of LMs in linguistic tasks is a novel method introduced in this paper .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate the hypothesis that language models can generalize from indirect evidence across a wide range of linguistic phenomena using the Filtered Corpus Training (FiCT) methodology. The study investigates whether language models are capable of extrapolating linguistic rules learned from training data to unseen environments by filtering out specific linguistic environments from the training corpus and comparing the performance of models trained on the filtered data with those trained on the full corpus . The results demonstrate that language models exhibit a strong ability to generalize from indirect evidence, even when tested on specific benchmarks corresponding to the environments that were removed from their input data .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper introduces the Filtered Corpus Training (FiCT) methodology as a novel approach to language model training and evaluation . FiCT involves filtering out sentences containing specific linguistic constructions of interest and training a new language model on this filtered corpus. The performance of the model is then assessed through targeted syntactic evaluations to determine its ability to generalize from related constructions to unseen ones .
FiCT aims to address the challenge of evaluating language models' capacity to make sophisticated linguistic generalizations based on indirect evidence . By focusing on specific linguistic phenomena and training models on filtered data, FiCT provides a way to measure the model's performance in a more targeted manner, beyond traditional evaluation metrics like perplexity .
The paper also discusses the importance of augmenting perplexity evaluations with other assessments that specifically target the models' ability to generalize in a human-like manner . This approach involves treating language models as participants in psycholinguistic paradigms to understand what these models "know" about specific linguistic phenomena .
Furthermore, the paper emphasizes the need to explore the connection between perplexity and human-like linguistic behavior, highlighting a dissociation between perplexity and certain aspects of linguistic performance . This suggests that evaluating language models solely based on perplexity may not capture their full capacity for making human-like linguistic generalizations . The Filtered Corpus Training (FiCT) methodology offers several key characteristics and advantages compared to previous methods outlined in the paper :
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Methodology Overview: FiCT involves training language models on filtered corpora that exclude sentences containing specific linguistic constructions of interest. This approach allows for targeted evaluation of the model's ability to generalize from related constructions to unseen ones .
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Performance Evaluation: FiCT introduces a novel way to assess language models' performance by comparing models trained on filtered data to those trained on the full corpus. This method focuses on the difference in performance between models that are identical except for their training data, providing insights into the models' generalization capabilities .
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Generalization Testing: The primary goal of FiCT is to test the language models' capacity to extrapolate linguistic rules learned from training data to unseen environments. By filtering out specific linguistic environments from the training data, FiCT aims to determine when and how language models can make such generalizations .
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Evaluation Metrics: FiCT utilizes a range of evaluation metrics, including perplexity, accuracy on benchmarks, and a novel metric called accΔ, to assess the models' performance in linguistic generalization. This comprehensive evaluation approach goes beyond traditional metrics to measure the models' ability to generalize in a human-like manner .
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Comparative Analysis: FiCT demonstrates that while Transformers may outperform LSTMs in terms of perplexity, both models exhibit equally strong performance in linguistic generalization measures. This suggests that language models, regardless of architecture, are capable of generalizing effectively from indirect evidence .
Overall, the FiCT methodology provides a structured and targeted approach to evaluating language models' generalization capabilities, offering insights into their performance beyond traditional metrics like perplexity.
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research studies exist in the field of language models and computational linguistics. Noteworthy researchers in this area include Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen, Marco Baroni, John Hale, Yiding Hao, Simon Mendelsohn, Rachel Sterneck, Randi Martinez, Robert Frank, among others . These researchers have contributed to various aspects of language modeling, syntactic evaluation, psycholinguistic modeling, and neural language models.
The key to the solution mentioned in the paper "Filtered Corpus Training (FiCT) Shows that Language Models can Generalize from Indirect Evidence" involves the use of filtered corpora to train language models. The study demonstrates that language models can generalize from indirect evidence, showcasing the effectiveness of training models on filtered data . This approach allows for improved performance and accuracy in language tasks by focusing on specific linguistic phenomena and areas of interest .
How were the experiments in the paper designed?
The experiments in the paper were designed with the following methodology:
- The models, including LSTMs and Transformers, were constructed with specific dimensions and parameters, such as feed-forward and hidden layer dimensions, attention heads, and hidden layers .
- Each model was trained on a single A40 GPU for 40 epochs using mixed-precision training and the AdamW optimization algorithm with specific hyperparameters like an initial learning rate of 5 × 10−5 and a batch size of 32 .
- The evaluation of the models was based on four primary metrics, including perplexity over the test corpus, accuracy on benchmarks in the BLiMP challenge set, and a novel metric called acc∆, which measures the difference in performance between models trained on different data .
- The Filtered Corpus Training (FiCT) methodology was employed to test the models' ability to generalize linguistic rules learned from training data to unseen environments by training models on data with specific linguistic constructions filtered out .
- The filters used in the experiments targeted various linguistic phenomena by removing sentences with specific linguistic features from the training corpus, allowing for the assessment of the models' generalization capabilities .
- The experiments aimed to determine if and how language models can make generalizations from indirect evidence by comparing models trained on ablated data with models trained on the full, naturalistic corpus .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is the English Wikipedia corpora released by Gulordava et al. (2018) . The code used in the study is not explicitly mentioned to be open source in the provided context.
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study conducted a wide range of experiments to test the ability of language models to generalize from indirect evidence . The findings indicate that language models can develop sophisticated linguistic generalizations without strict reliance on direct attestation of linguistic evidence . The research demonstrates that learners can leverage indirect sources of evidence to arrive at correct generalizations across various syntactic and semantic phenomena . Despite some negative effects on performance for specific linguistic evaluations, the overall results show that models still perform considerably better than chance, supporting the hypothesis of indirect evidence .
Moreover, the methodology used in the study, Filtered Corpus Training (FiCT), effectively tested the models' capability to extrapolate linguistic rules learned from training data to unseen environments . By comparing models trained on ablated data with models trained on the full corpus, the study was able to determine how language models make generalizations and under what conditions . The filters applied to remove specific linguistic environments from the training data allowed for a clear assessment of the models' ability to generalize from indirect evidence .
Overall, the experiments and results in the paper provide robust support for the scientific hypotheses under investigation. The use of FiCT methodology, the comprehensive range of linguistic phenomena analyzed, and the confirmation of the indirect evidence hypothesis all contribute to the strong scientific validity of the study's findings .
What are the contributions of this paper?
The contributions of this paper are outlined based on the Contributor Role Taxonomy (CRediT) :
- Abhinav Patil contributed to conceptualization, methodology, software, formal analysis, investigation, data curation, writing (original draft and review), visualization, and supervision.
- Jaap Jumelet contributed to conceptualization, methodology, software, formal analysis, data curation, writing (original draft and review), visualization, and supervision.
- Yu Ying Chiu contributed to software, data curation, and writing (review and editing).
- Andy Lapastora contributed to methodology, software, investigation, data curation, and writing (review and editing).
- Peter Shen contributed to software, data curation, and writing (review and editing).
- Lexie Wang, Clevis Willrich, and Shane Steinert-Threlkeld also made significant contributions to the paper.
What work can be continued in depth?
Further research in the field can delve deeper into the linguistic generalization abilities of language models by exploring the impact of filtered corpus training on grammaticality judgments . This entails investigating whether language models can form intricate linguistic generalizations solely based on indirect evidence, without the need for direct exposure to specific constructions during training . Additionally, there is potential to analyze the inductive biases of different major language model architectures, such as Transformers and LSTMs, to gain a more detailed understanding of their performance on a fine-grained linguistic level . By extending the study to explore the dissociation between perplexity and linguistic generalization, researchers can uncover valuable insights into the capabilities and limitations of these models in processing and understanding language .