Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction

• URL: http://arxiv.org/abs/2606.28544v1
• Date: Fri, 26 Jun 2026 18:57:22 GMT
• Title: Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction
• Abstract summary: We operationalize a taxonomy of eight communication roles grounded in education literature.<n>We annotate a corpus of 6,307 Slack messages from 55 students across 18 teams in a semester-long computer science course project.
• Score: 14.751997401312664
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Team roles offer an interpretable lens on collaboration, yet computational studies of roles often rely on domain-specific personas or data-driven clustering rather than theory-grounded taxonomies. We operationalize a taxonomy of eight communication roles grounded in education literature and annotate a corpus of 6,307 Slack messages from 55 students across 18 teams in a semester-long computer science course project. We evaluate whether LLMs can approximate expert labels, enabling scalable, taxonomy-driven role annotation. Using these role labels, we characterize role dynamics over teams' lifecycles, finding that different roles peak at different moments and that students enact a more diverse set of roles as projects progress. To evaluate the utility of our role constructs, we use them to predict peer recognition, outperforming lexical, conversational, and LLM-prompting baselines. To assess generalizability beyond the educational context, we apply the same role constructs to a public dataset (DeliData) to predict team performance improvement after deliberation, again exceeding prior performance.

Related papers

• Character Beyond Speech: Leveraging Role-Playing Evaluation in Audio Large Language Models via Reinforcement Learning [57.22705949022221]
  RoleJudge is an evaluation framework that leverages audio large language models to assess the alignment between speech and character.<n>We introduce RoleChat, the first voice role-playing evaluation dataset enriched with chain-of-thought reasoning annotations.
  arXiv  Detail & Related papers  (2026-04-15T12:39:03Z)
• How role-play shapes relevance judgment in zero-shot LLM rankers [15.11127856890218]
  Large Language Models (LLMs) have emerged as promising zero-shot rankers.<n>Their performance is highly sensitive to prompt formulation.<n>In particular, role-play prompts, where the model is assigned a functional role or identity, often give more robust and accurate relevance rankings.
  arXiv  Detail & Related papers  (2025-10-20T13:39:48Z)
• SpeechRole: A Large-Scale Dataset and Benchmark for Evaluating Speech Role-Playing Agents [72.79816494079833]
  Role-playing agents have emerged as a promising paradigm for achieving personalized interaction and emotional resonance.<n>Existing research primarily focuses on the textual modality, neglecting the critical dimension of speech in realistic interactive scenarios.<n>We construct SpeechRole-Data, a large-scale, high-quality dataset that comprises 98 diverse roles and 112k speech-based single-turn and multi-turn conversations.
  arXiv  Detail & Related papers  (2025-08-04T03:18:36Z)
• How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective [64.00022624183781]
  Large language models (LLMs) can assess relevance and support information retrieval (IR) tasks.<n>We investigate how different LLM modules contribute to relevance judgment through the lens of mechanistic interpretability.
  arXiv  Detail & Related papers  (2025-04-10T16:14:55Z)
• RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following [31.80357046048002]
  Role-playing is important for Large Language Models to follow diverse instructions.<n>Existing role-playing datasets mostly contribute to controlling role style and knowledge boundaries.<n>We introduce a fine-grained role-playing and instruction-following benchmark, named RoleMRC.
  arXiv  Detail & Related papers  (2025-02-17T03:08:37Z)
• ERABAL: Enhancing Role-Playing Agents through Boundary-Aware Learning [17.5855800570993]
  Role-playing is an emerging application in the field of Human-Computer Interaction (HCI) Despite significant progress, role-playing agents (RPLAs) still struggle with maintaining role-consistency across conversations. We present ERABAL, a framework aimed at enhancing RPLAs' role-playing capabilities through boundary-aware learning.
  arXiv  Detail & Related papers  (2024-09-23T05:12:13Z)
• Thinking Before Speaking: A Role-playing Model with Mindset [0.6428333375712125]
  Large Language Models (LLMs) are skilled at simulating human behaviors. These models tend to perform poorly when confronted with knowledge that the assumed role does not possess. We propose a Thinking Before Speaking (TBS) model in this paper.
  arXiv  Detail & Related papers  (2024-09-14T02:41:48Z)
• Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions [62.0123588983514]
  Large Language Models (LLMs) have demonstrated wide-ranging applications across various fields. We reformulate the peer-review process as a multi-turn, long-context dialogue, incorporating distinct roles for authors, reviewers, and decision makers. We construct a comprehensive dataset containing over 26,841 papers with 92,017 reviews collected from multiple sources.
  arXiv  Detail & Related papers  (2024-06-09T08:24:17Z)
• Role-playing Prompt Framework: Generation and Evaluation [3.2845546753303867]
  Large language models (LLMs) exhibit impressive proficiency in natural language generation, understanding user instructions, and emulating human-like language use.<n>This paper introduces a prompt-based framework designed to leverage GPT's capabilities for the generation of role-playing dialogue datasets.
  arXiv  Detail & Related papers  (2024-06-02T06:09:56Z)
• SocialBench: Sociality Evaluation of Role-Playing Conversational Agents [85.6641890712617]
  Large language models (LLMs) have advanced the development of various AI conversational agents. SocialBench is the first benchmark designed to evaluate the sociality of role-playing conversational agents at both individual and group levels. We find that agents excelling in individual level does not imply their proficiency in group level.
  arXiv  Detail & Related papers  (2024-03-20T15:38:36Z)
• Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment [62.898963074989766]
  We introduce Ditto, a self-alignment method for role-play. This method creates a role-play training set comprising 4,000 characters, surpassing the scale of currently available datasets by tenfold. We present the first comprehensive cross-supervision alignment experiment in the role-play domain.
  arXiv  Detail & Related papers  (2024-01-23T03:56:22Z)
• RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models [107.00832724504752]
  We introduce RoleLLM, a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models (LLMs) By Context-Instruct and RoleGPT, we create RoleBench, the first systematic and fine-grained character-level benchmark dataset for role-playing with 168,093 samples.
  arXiv  Detail & Related papers  (2023-10-01T17:52:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.