Communication Dans Un Congrès Année : 2025

Language Style Matching in Large Language Models

Résumé

Language Style Matching (LSM)-the subconscious alignment of linguistic style between conversational partners-is a key indicator of social coordination in human dialogue. We present the first systematic study of LSM in Large Language Models (LLMs) focusing on two primary objectives: measuring the degree of LSM exhibited in LLM-generated responses and developing techniques to enhance it. First, in order to measure whether LLMs natively show LSM, we computed LIWC-based LSM scores across diverse interaction scenarios and found that LSM scores for text generated by LLMs were either below or near the lower range of such scores observed in human dialogue. Second, we show that LLMs' adaptive behavior in this regard can be improved using inference-time techniques. We introduce and evaluate an inference-time sampling strategy-Logit-Constrained Generation-which can substantially enhance LSM scores in text generated by an LLM while preserving fluency. By advancing our understanding of LSM in LLMs and proposing effective enhancement strategies, this research contributes to the development of more socially attuned and communicatively adaptive AI systems.

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hal-05602061 , version 1 (24-04-2026)

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  • HAL Id : hal-05602061 , version 1

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Noé Durandard, Saurabh Dhawan, Thierry Poibeau. Language Style Matching in Large Language Models. Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Aug 2025, Avignon, France. ⟨hal-05602061⟩
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