Improving Sentence Judgement in Language Models

This article looks into how Transformer language models work when it comes to checking if sentences make sense. The focus is on a type of language model called BERT and a specific task of judging if sentences are useful or not. Our method uses a technique called topological data analysis to examine the connections made by Attention in language models.

#What We Are Studying

We compare how BERT and other models do when determining if sentences in English and Russian are acceptable. We use two Datasets for this task: CoLA for English and RuCoLA for Russian. These datasets contain sentences that have been identified as being acceptable or not based on various grammatical issues. Some examples of sentence problems include errors in using verbs, the order of words, and the use of pronouns.

#The Importance of Attention in Language Models

Language models like BERT use something called attention to help them focus on different parts of a sentence. This means they can weight different words or phrases differently depending on their importance in forming meaning. We create directed graphs based on this attention to understand how these models work when judging sentences.

In our work, we introduce two new Features that can help improve how well these models classify sentences. These features deal with the structure of the attention graphs created by the models. By analyzing these graphs, we can gain insights into how the models understand language.

#Why This Matters

Understanding how language models process language is crucial for improving their performance. We learned that fine-tuning BERT for specific tasks can sometimes lead to a loss of general knowledge that it had from the beginning. This is particularly relevant for models working with languages that have free word order, like Russian, where meaning can be conveyed in multiple ways.

#Results and Findings

Our research shows that models fine-tuned with TDA methods perform better at judging sentences than those that rely solely on traditional training methods. Specifically, we found that TDA-based classifiers, which use the features derived from attention graphs, yield better results.

In both English and Russian, the TDA-based classifiers showed notable improvements in their ability to classify sentences. For example, the models trained with the new topological features had a marked improvement in their scores, indicating that these features are capturing important language information.

#Examining Acceptability Judgments

We looked closely at how well different models did at determining whether sentences from the datasets were acceptable. The results showed that models using our TDA-based features outperformed others. This performance boost was particularly clear in sentences that contained grammatical errors.

For instance, we found that models trained on Russian data were significantly better at identifying sentences with specific syntactic issues than those that were not. This suggests that TDA can help highlight the elements of language that are often overlooked in traditional models.

#How Models Handle Errors

When analyzing how well these models perform, we saw that they were often confused by complex sentences, especially those containing multiple clauses or named entities. This indicates that while models have become highly skilled at understanding language, they can still struggle with more complex structures.

To better understand where models get things wrong, we examined the types of errors they made. We found that many misclassifications were due to challenges in dealing with longer sentences that had intricate grammatical constructions.

#The Role of Attention Heads

An interesting aspect of our research was the exploration of attention heads within the models. Each head can pay attention to different parts of a sentence, allowing the model to capture various linguistic features. We discovered that certain heads are more important for making correct predictions, while others lead to errors.

By assessing the influence of different heads, we could see which parts of the model were focusing on useful linguistic elements and which were not. This is vital for improving the models' understanding and for potential future applications in different languages and contexts.

#Conclusion

The findings of this study point to the potential of using TDA to improve language models significantly. By introducing new ways to analyze attention in these models, we can better understand how they process language and what makes them succeed or fail at tasks like acceptability classification.

As we continue to study these models, we hope that this work will lead to improved understanding and performance in language tasks, especially in less-explored languages like Russian. Exploring these models with new techniques will contribute to better tools for language processing in various applications in the future.

Our results encourage further exploration of TDA applications. We believe these methods can help advance how language models work across different languages, making them more effective and reliable. With continued development, the integration of topological data analysis and language models could lead to even more accurate and nuanced understandings of how humans use language.