LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context
  Memory of Transformers
Authors: name: Anton Razzhigaev, name: Matvey Mikhalchuk, name: Temurbek Rahmatullaev, name: Elizaveta Goncharova, name: Polina Druzhinina, name: Ivan Oseledets, name: Andrey Kuznetsov

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.