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AI Research
arXiv CS.AI·May 28·5 min read
The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Models
TL;DR
Language models like Transformers often lack interpretability regarding their syntactic knowledge, which is crucial for understanding their decision-making. A systematic review was conducted to analyze existing research on this interpretability, focusing on how syntactic structures are represented in these models.
✦ Why It Matters
Engineers can leverage insights from this review to develop more interpretable language models that better understand syntax.
Key Takeaways
- Explore syntactic capabilities of TLMs beyond English.
- Investigate performance on syntax-semantics tasks.
- Incorporate diverse methodologies in future studies.
- Focus on languages with less digital support.
Full Summary
This review analyzes 337 articles assessing the syntactic capabilities of Transformer-based language models (TLMs), compiling over 3,000 data points across various syntactic phenomena, languages, and methodologies. Results indicate that TLMs exhibit a considerable amount of syntactic knowledge, performing well on formal syntactic tasks but showing inconsistent results on tasks that bridge syntax and semantics.
Performance is notably lower for languages with less digital presence. Probing studies further confirm the existence of syntactic knowledge, yet the current research landscape is methodologically diverse and primarily focused on English and BERT-like models.
Consequently, insights into the computational mechanisms of syntactic processing remain limited. The authors suggest future research should address these gaps and explore a wider range of languages and models.