Linguistically Grounded Analysis of Language Models using Shapley Head Values
This provides insights into how language models organize linguistic information, supporting the hypothesis of learned subnetworks, which is incremental for interpretability in NLP.
The paper tackled the problem of understanding how linguistic knowledge is encoded in language models by analyzing morphosyntactic phenomena using Shapley Head Values on BERT and RoBERTa with the BLiMP dataset, and found that attention heads cluster based on related linguistic constructions, revealing distinct patterns across models.
Understanding how linguistic knowledge is encoded in language models is crucial for improving their generalisation capabilities. In this paper, we investigate the processing of morphosyntactic phenomena, by leveraging a recently proposed method for probing language models via Shapley Head Values (SHVs). Using the English language BLiMP dataset, we test our approach on two widely used models, BERT and RoBERTa, and compare how linguistic constructions such as anaphor agreement and filler-gap dependencies are handled. Through quantitative pruning and qualitative clustering analysis, we demonstrate that attention heads responsible for processing related linguistic phenomena cluster together. Our results show that SHV-based attributions reveal distinct patterns across both models, providing insights into how language models organize and process linguistic information. These findings support the hypothesis that language models learn subnetworks corresponding to linguistic theory, with potential implications for cross-linguistic model analysis and interpretability in Natural Language Processing (NLP).