9 papers
Through the Lens of Character: Resolving Modality-Role Interference in Multimodal Role-Playing Agent
Yihong Tang, Kehai Chen, Xuefeng Bai +1
The advancement of Multimodal Large Language Models (MLLMs) has expanded Role-Playing Agents (RPAs) into visually grounded environments. However, human vision is inherently subject…
Character-R1: Enhancing Role-Aware Reasoning in Role-Playing Agents via RLVR
Yihong Tang, Kehai Chen, Xuefeng Bai +4
Current role-playing agents (RPAs) are typically constructed by imitating surface-level behaviors, but this approach lacks internal cognitive consistency, often causing out-of-char…
Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents
Yihong Tang, Kehai Chen, Liang Yue +11
With the rise of large language models (LLMs), LLM agents capable of autonomous reasoning, planning, and executing complex tasks have become a frontier in artificial intelligence.…
MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching
Liang Yue, Yihong Tang, Kehai Chen +2
Instruction fine-tuning is crucial in NLP tasks, enhancing pretrained models' instruction-following capabilities and task-specific performance. However, obtaining high-quality fine…
ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities
Yifan Duan, Yihong Tang, Kehai Chen +2
High-quality prompts are crucial for eliciting outstanding performance from large language models (LLMs) on complex tasks. Existing research has explored model-driven strategies fo…
Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
Yihong Tang, Kehai Chen, Muyun Yang +4
The advancement of Large Language Models (LLMs) has spurred significant interest in Role-Playing Agents (RPAs) for applications such as emotional companionship and virtual interact…