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

2025-02-20

AI Summary

This study investigates how large language models (LLMs) encode long-range contextual information, focusing on the functional role of low-salience tokens—such as articles and punctuation—in contextual memory. Method: We introduce “contextual linearity” as a novel analytical dimension, quantified via logit lens visualization, token-level nonlinearity measurement, intrinsic dimension estimation, and inter-layer embedding linear approximation. Contribution/Results: We find a strong negative correlation between contextual linearity and model memory capacity; removing low-salience tokens significantly impairs long-range understanding—evidenced by consistent performance degradation on MMLU and BABILong-4k. Crucially, we provide the first systematic evidence that stop words are not redundant but serve as essential carriers sustaining the nonlinearity required for robust contextual modeling. To support fine-grained diagnostic analysis of context memory, we open-source LLM-Microscope, a toolkit enabling token- and layer-level interpretability.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Quantify LLMs' encoding of contextual information.
• Reveal high context carried by minor tokens.
• Assess token-level nonlinearity and contextual memory.

Innovation

• Quantifies punctuation's context role
• Links contextualization to linearity
• Offers open-source LLM analysis toolkit