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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