Language Style Matching in Large Language Models (2025)

Abstract: 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.

## introduction two humans in conversation rarely behave independently of each other. instead, they show a striking tendency to unconsciously match the other's verbal and non-verbal behavior. such automatic coordination between dialogue partners has been shown to be a fundamental feature of human communication across multiple behavioral dimensions. in the non-verbal realm, such alignment has been shown for posture, mannerisms, facial expressions, crossing of arms and nodding of heads @xcite @xcite . in the verbal realm, such behavior has been shown in an even wider rangecoordination of acoustic features such as accent, speech rate, and pitch @xcite , pause length @xcite , lexico-syntactic priming for adjacent utterances @xcite @xcite . in language style matching (lsm), the best studied example of such communicative coordination, individuals in conversation adjust the number of function words such as articles, prepositions, pronouns etc. in their responses to mirror the usage of such words in their partner's previous statement @xcite @xcite @xcite . function words are known to reliably reflect speakers' psychological states and traits @xcite and unlike content words-such as nouns and verbs-they carry minimal meaning outside of context and are processed quickly and mostly unconsciously @xcite . when two people talking to each other, align their language styles (characterized by high lsm scores), they tend to benefit in a number of ways. they tend to collaborate more effectively in the short term and maintain longer-lasting relationships, both platonic and romantic. for instance, studies show that coworkers, whether in experimental settings or real-life workplaces, express greater mutual liking when their language styles align during collaboration @xcite . similarly, the stability of both new and long-term romantic relationships increases when partners naturally synchronize their language use in conversation @xcite . according to the communi-cation accommodation theory, people match each other's communication styles to be liked and increase rapport @xcite and such matching has been suggested to act as social glue that binds together pairs and groups, and strengthen their social ties @xcite . lsm is thus a critical indicator of social attunement, reflecting alignment and mutual understanding in interactions. as large language models (llms) come to be rapidly deployed in inherently social settings ranging from education to mental health, it is clear that to see llms purely in terms of information exchange is not enough. we posit that coordination and alignment with an individual user's verbal behavior would be crucial to any artificially intelligent system's ability to emulate natural human communication. this study investigates the phenomenon of language style matching in large language models. we first measure lsm in llm interactions in a range of llms using both publicly available llm chat datasets recorded in the wild, and those generated by us for the purpose of this experiment using a literary completion task. subsequently, we demonstrate several approaches (e.g. logit-constrained generation) that can be used to boost lsm in llm interactions. it's important to note that lsm has been shown not only in face-to-face human conversations, but also for indirect conversations in online chats @xcite , social media @xcite , and even letters sent far apart in time @xcite . it can thus be assumed to be an integral social aspect of human communication in all its forms whether it happens face-to-face or online or in a human-llm chat. by evaluating lsm in llms, we hope to better understand their capacity to engage users in meaningful, context-sensitive dialogues, fostering trust and enhancing user experience. moreover, insights from this research can guide the development of more adaptive and responsive conversational agents, making them more effective in a wide range of applications, from education to mental health support. ## related works linguistic coordination at the lexical level -as measured by lsm-remains largely unexplored in the llm literature. however, this study aligns with broader research directions exploring the behaviors of llms. on one hand, given the social dynamics associated with lsm, it resonates strongly with studies on llms' social behaviors and adaptive capabilities. on the other hand, lsm's design directly relates to research analyzing the linguistic characteristics of model-generated texts. ## language style matching (lsm) we adopt the canonical language style matching (lsm) metric originally introduced by pennebaker and colleagues @xcite because it has two key methodological advantages. first, by focusing exclusively on function-word categories, the measure captures subconscious stylistic coordination while remaining largely independent of topical content, enabling valid comparisons across speakers, tasks, and-crucially for our work-across human and llm dialogue domains. second, extensive validation work in existing literature shows that this exact composite lsm score predicts interpersonal rapport, relationship formation, and group cohesion in settings ranging from laboratory chats to speed-dating and small-group problem solving (see, for example, @xcite al. (2011); gonzales et al. (2010) ) establishing both its external validity and its status as the de-facto benchmark in the literature. hence, we retain this standard metric for our evaluation of llm-generated text. ## observed lsm behavior the availability of datasets collecting real-world conversations between users and chat assistants offers a compelling framework to study lsm in organic settings. however, these datasets are inherently diverse, encompassing a wide range of topics, writing styles, user behaviors, etc. plus, conversations in such contexts often involve code-switching -shifts between languages, styles, and/or topicswhich poses challenges in interpreting lsm scores reliably. to alleviate these complexities, this study complements the analysis of "in-the-wild" conversations from publicly available datasets of llm-human chats with controlled experiments inspired by traditional lsm research. this dual approach aims at providing a more comprehensive understanding of lsm in interactions with llms. ## increasing llm's lsm scores finally, this section explores methods to enhance the lsm score of model-generated responses. the presented endeavors focus on inference-time strategies, covering two main approaches: prompt engineering and model engineering. the ensuing strategies are evaluated in diverse scenarios. ## conclusions and discussion human to human communicative behaviors are "patterned and coordinated, like a dance" @xcite . human-llm communication has, on the other hand, been modeled mostly as asocial information exchange. a range of researchers have emphasized the importance of incorporating social aspects of language, especially now as llm are increasingly being deployed in real world settings @xcite . this study furthers that objective by examining language style matching, the best known example of human communicative coordination, in large language models, and by developing techniques to enhance it. first, we examined whether llms natively show lsm in state-of-the-art llms across diverse interaction scenarios, ranging from real-world dialogues to controlled experimental settings. we measured lsm scores for both publicly available datasets of human-llm interactions, and also for text completion tasks in a controlled laboratory setting. we found lsm scores in text generated by a range of llms to be either quite low or at the lower range of such scores observed in human dialogue and writing (james pennebaker, 2022) (figure 1 ). for individual function-word categories revealed a conserved pattern for specific categories -e.g. lsm score for prepositions was consistently higher while that for negations was lower across various models and contexts (figure 4 ). we found no effect for model size -larger models don't show better lsm scores -suggesting lsm is not a behavior emerging with scale (figure 3 ). this might be because larger instruction-tuned models come to have stronger intrinsic style with diminished flexibility for communicative coordination. second, we introduce and evaluate several inference-time strategies to improve lsm scores of llm generated text. we evaluated persona prompting (by crafting an ideal persona that embodies the ideal characteristics associated with higher lsm scores based on extensive psycho-linguistic literature) and objective-focussed prompting (describing the lsm computation and instructing the model to generate text that would show the highest possible lsm scores). we found both prompt engineering approaches to improve lsm scores ( human cognition treats linguistic style elements that are aligned to match the other as a useful behavioral marker of social and psychological alignment in various interpersonal and group dynamics. we believe incorporating such communicative coordination in llm text generation would make them a better fit for ai systems designed to interact with humans.