Punctuation and Predicates in Language Models

2025-08-11

AI Summary

This study investigates the mechanisms of information acquisition and propagation in large language models (LLMs), focusing on the computational role of punctuation, hierarchical representation of linguistic constituents (e.g., subjects, adjectives, sentences), and differential processing of logical constructs—specifically conditional (if-then) and universal quantification (for all). Using targeted intervention analysis, inter-layer swapping, and layer replacement experiments, we systematically examine information flow and reasoning structure across GPT-2, DeepSeek, and Gemma. Our findings reveal: (1) punctuation exhibits model-specific necessity and sufficiency, serving as a memory anchor in certain architectures; (2) linguistic constituents are not statically encoded in early layers but dynamically engage in multi-layer interactions; (3) conditional and universal quantification induce distinct activation patterns and path dependencies in deeper layers. These results uncover structured information flow and logic-sensitive computation in LLM inference, advancing our understanding of how syntactic and semantic cues govern reasoning.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In this paper we explore where information is collected and how it is propagated throughout layers in large language models (LLMs). We begin by examining the surprising computational importance of punctuation tokens which previous work has identified as attention sinks and memory aids. Using intervention-based techniques, we evaluate the necessity and sufficiency (for preserving model performance) of punctuation tokens across layers in GPT-2, DeepSeek, and Gemma. Our results show stark model-specific differences: for GPT-2, punctuation is both necessary and sufficient in multiple layers, while this holds far less in DeepSeek and not at all in Gemma. Extending beyond punctuation, we ask whether LLMs process different components of input (e.g., subjects, adjectives, punctuation, full sentences) by forming early static summaries reused across the network, or if the model remains sensitive to changes in these components across layers. Extending beyond punctuation, we investigate whether different reasoning rules are processed differently by LLMs. In particular, through interchange intervention and layer-swapping experiments, we find that conditional statements (if, then), and universal quantification (for all) are processed very differently. Our findings offer new insight into the internal mechanisms of punctuation usage and reasoning in LLMs and have implications for interpretability.

Problem

• Investigating punctuation tokens as computational elements in LLMs
• Determining if LLMs form static summaries or remain layer-sensitive
• Analyzing differential processing of conditional and universal statements

Innovation

• Intervention-based techniques evaluate punctuation tokens
• Layer-swapping experiments analyze conditional statement processing
• Interchange intervention assesses universal quantification mechanisms