Papers with role-playing agents

6 papers
CharacterGPT: A Persona Reconstruction Framework for Role-Playing Agents (2025.naacl-industry)

Copied to clipboard

Challenge: Maintaining consistent character personas remains a significant challenge due to variability in information extraction.
Approach: They propose a framework to dynamically reconstruct character personas through Character Persona Training.
Outcome: The proposed framework is evaluated through Big Five personality evaluations and creative tasks, in which characters generate original narratives.
SocialBench: Sociality Evaluation of Role-Playing Conversational Agents (2024.findings-acl)

Copied to clipboard

Challenge: Existing studies on role-playing agents have focused on enhancing their conversational capability, role-specific knowledge and style, but there has been a gap in assessing their social intelligence.
Approach: They propose a benchmark to evaluate the sociality of role-playing agents using LLMs.
Outcome: The proposed benchmark is constructed from various sources and covers a wide range of 500 characters and over 6,000 question prompts and 30,800 multi-turn role-playing utterances.
TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models (2024.findings-acl)

Copied to clipboard

Challenge: Large Language Models (LLMs) can be used to simulate human behaviors, but point-in-time role-playing is a key component of fandom role-players.
Approach: They propose a benchmark to evaluate point-in-time character hallucination in role-playing LLMs.
Outcome: The proposed method reduces point-in-time character hallucinations effectively by decomposing reasoning steps and using narrative experts.
CharacterCraft: Bridging the Literature-Reality Dialogue Gap for Practical Role-Playing Agents (2025.findings-emnlp)

Copied to clipboard

Challenge: Existing dialogue datasets have a bias between query distributions and real-world user language usage.
Approach: They propose a framework for Chinese role-playing and a robust evaluation method . they propose specialized Chinese dialogue extraction model and specialized memory retrieval module .
Outcome: The proposed framework extracts character dialogue from novels and ensures high data quality.
Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works (2024.emnlp-main)

Copied to clipboard

Challenge: Recent advances in large language models (LLMs) have catalyzed numerous AI applications, among which role-playing agents (RPAs) are particularly popular.
Approach: They propose to evaluate LLMs' character understanding capability via the character profiling task, i.e., summarizing character profiles from corresponding materials, a widely adopted yet understudied practice for RPA development.
Outcome: The proposed model outperforms existing models and literature summarization methods and proves its ability to understand fictional characters in downstream tasks.
ChatAnime: Towards User-Centered Emotional Support in LLM-based Virtual Character Chat (2026.acl-long)

Copied to clipboard

Challenge: Existing research focuses on character consistency in fictional or game-based scenarios . ESRP framework is designed to align role-playing with real-world user scenarios based on emotional needs.
Approach: They propose a framework to align role-playing with real-world user scenarios and emotional needs.
Outcome: The proposed framework aligns role-playing with real-world user scenarios and emotional needs.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations