LLM s stick to the point, humans to style: Semantic and Stylistic Alignment in Human and LLM Communication (2025)

Abstract: This study investigates differences in linguistic accommodation—changes in language use and style that individuals make to align with their dialogue partners—in human and LLM communication. Specifically, it contrasts semantic and stylistic alignment within question-answer pairs in terms of whether the answer was given by a human or an LLM. Utilizing embedding-based measures of linguistic similarity, we find that LLM-generated answers demonstrate higher semantic similarity—reflecting close conceptual alignment with the input questions—but relatively lower stylistic similarity. Human-written answers exhibit a reverse pattern, with lower semantic but higher stylistic similarity to the respective questions. These findings point to contrasting linguistic accommodation strategies evident in human and LLM communication, with implications for furthering personalization, social attunement, and engagement in human-AI dialogue.

## introduction human dialogue comes with a range of social and psychological adaptations @xcite @xcite , that are central to mutual understanding and social attunement between two dialogue partners @xcite @xcite . much as dialogue with large language models (llms) has come to offer a close approximation to that with humans, it remains unclear whether or not such adaptive behaviors might already have been learned by llms owing to their training on massive amounts of human-produced texts. as llms are increasingly being deployed in what are essentially social settings, such as teaching and mental health counseling, understanding of these socially adaptive behaviors has assumed greater relevance @xcite . linguistic accommodation is one such behavior @xcite . it refers to adjustments in language use and style that individuals make to align with their dialogue partners. it has been shown to be a distinctive feature of human dialogue and plays an important role in effective interaction, facilitating social approval and mutual understanding @xcite . a range of recent studies have compared linguistic differences between human and llm generated text. @xcite compared ai-created and human-created misinformation and found that aigenerated misinformation exhibited enhanced emotional content and used more salient expressions. @xcite compared human-written versus chatgpt-generated argumentative student essays and found that expert teachers rated the ones by chatgpt higher. they also found that while humans used more modals and epistemic markers, chatgpt wrote more complex sentences and used more nominalizations. @xcite compared human-written english news text with that generated by llms, and found that human texts exhibit more scattered sentence length distributions, a distinct use of dependency and constituent types, and more aggressive emotions (fear, disgust), while llm outputs showed more markers of objective language. @xcite conducted a range of psycholinguistic tests and found that, compared to llms, humans, preferred using shorter words to convey less informative content, and showed higher use of context to resolve syntactic ambiguities. while the aforementioned studies compared linguistic properties of human and llm generated text corpora, in this study, we compare linguistic properties within specific question-answer pairs, where the answers were given either by a human or an llm. toward this purpose, we extend a dataset of human-human and human-llm qa pairs @xcite , to a range of new llms. subse-quently, using embedding-based measures, we calculate semantic (solatorio, 2024) and stylistic @xcite similarities between each qa pair. this allows us to examine whether humans and llms make adjustments to the language style and usage in their responses, in order to better align with the style and usage evident in the questions from their dialogue partners. ## methods the developed analytical framework proposes to study linguistic alignment within human-human and human-llm qa pairs through their vector representations within high dimensional textual embedding spaces. ## results & discussion figure 1 showcases the results of the analysis. the cosine similarities, displayed as bars, and specificity scores, displayed as red triangles, reveal contrasting linguistic alignment strategies between hu-man and llm communication. at the same time, various llms (from different model families and sizes) that we tested, do not seem to differ from each other in their alignment strategies. ## conclusion this study contributes to a growing body of work examining how language models compare to humans in conversational settings. it analyzes ques-tion answering approaches in llms and contrasts them with human behavior by introducing h-llmc2, an augmented version of a sample of hc3, a corpus of human-written questions and parallel sets of respective answers written by humans and various llms. the similarity and specificity of responses are assessed within representation spaces encoding semantics and style. while no major distinctions are observed between various llms (of different sizes and model-families), the findings reveal interesting contrasts between llm-generated and human-written answers. llms tend to exhibit higher semantic similarity with high specificity, but don't reach human-level stylistic similarity. human-written answers exhibit a reverse pattern, with lower semantic but higher stylistic similarity to the respective questions (when questions lengths are long enough). these findings point to contrasting linguistic accommodation strategies evident in human and llm communication. these results invite further exploration into the conversational dynamics of llms, particularly with regard to how they differ from human behavior, and prompt reflection on strategies that could foster more natural communicative practices. inference from the extensive literature on linguistic accommodation suggests that bringing stylistic and semantic alignment strategies in llm responses closer to human behavior can enhance social attunement and engagement in human-ai dialogue.