ConvoDrift：用于建模对话风格语调演变的多轮对话数据集

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Abstract:The evolution of linguistic style in conversations is an underexplored issue in NLP. Most style-control datasets focus on sentences or assume a static style throughout, missing the dynamic shifts that occur as user preferences change during interactions. We introduce ConvoDrift, a dataset designed to model progressive stylistic conversational tone drift under fixed semantic intent. It is built on 15,727 shared multi-turn conversational structures for adaptation and persona-conditioned alignment methods. It consists of six prompt-response pairs per conversation, each with the annotation of style drift and style direction labels. These pairs cover a range of communication genres. We further derive a complementary pairwise dataset by pairing semantically equivalent but stylistically distinct responses and annotating persona-conditioned preferences using five distinct style communication personas, enabling the controlled study of personalisation and pluralistic alignment in language tone. In addition to dataset construction, we conduct a comprehensive evaluation involving human validation, LLM-as-judge assessment, and automatic lexical and semantic evaluations. Across seven Likert criteria annotated by three human annotators, the average Krippendorff's alpha is 0.88, and our lexical and semantic analyses show that drift events induce lexical changes while preserving semantic similarity.

Comments:	13 pages, 14 figures, 7 tables, Accepted paper at the 13th Conference on Computational Linguistics and Speech Processing (ROCLING) 2026
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2610.02873 [cs.CL]
	(or arXiv:2610.02873v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.02873 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Vihindi Kotalawala [view email]
[v1] Fri, 2 Oct 2026 06:11:40 UTC (14,202 KB)