LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context
  Memory of Transformers
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.
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Anton Razzhigaev
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2025-02-24T13:11:53.576Z
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Anton Razzhigaev
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Matvey Mikhalchuk
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Matvey Mikhalchuk
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Temurbek Rahmatullaev
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Temurbek
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Elizaveta Goncharova
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2025-02-24T13:12:27.998Z
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Elizaveta Goncharova
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Elizaveta
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Polina Druzhinina
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2025-02-24T13:12:33.837Z
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Polina Druzhinina
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Ivan Oseledets
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2025-02-24T13:12:40.295Z
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Ivan Oseledets
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oseledets
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Andrey Kuznetsov
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2025-02-25T09:40:42.629Z
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Andrey Kuznetsov
user
kuznetsoffandrey
LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context
  Memory of Transformers
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.
Analysis of Large Language Models reveals that even minor tokens are crucial for context, and introduces LLM-Microscope for assessing token-level nonlinearity and contextual memory.
Large Language Models LLMs contextual information tokens determiners punctuation stopwords articles commas MMLU BABILong-4k contextualization linearity embeddings Logit Lens intrinsic dimensionality long-range understanding