LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context Memory of Transformers

P. 7757–7764.

Anton R., Mikhalchuk M., Rahmatullaev T., Goncharova E., Druzhinina P., Oseledets I., Kuznetsov A.

We introduce methods to quantify how Large Language Models (LLMs) encode and store contextual information, revealing that tokens often seen as minor (e.g., determiners, punctuation) carry surprisingly high context. Notably, removing these tokens — especially stopwords, articles, and commas — consistently degrades performance on MMLU and BABILong-4k, even if removing only irrelevant tokens. Our analysis also shows a strong correlation between contextualization and linearity, where linearity measures how closely the transformation from one layer’s embeddings to the next can be approximated by a single linear mapping. These findings underscore the hidden importance of “filler” tokens in maintaining context. For further exploration, we present LLM-Microscope, an open-source toolkit that assesses token-level nonlinearity, evaluates contextual memory, visualizes intermediate layer contributions (via an adapted Logit Lens), and measures the intrinsic dimensionality of representations. This toolkit illuminates how seemingly trivial tokens can be critical for long-range understanding.

Keywords: NLP, интерпретируемость, interpretability, LLM, большие языковые модели, Обработка естественного языка (NLP)

In book

Findings of the Association for Computational Linguistics: NAACL 2025

Association for Computational Linguistics, 2025.