5 citations · 8 across the 6 of their papers we have counts for
7 papers
MetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling
Renhao Li, Xinjie Shen, Yang Su +4
Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging. Existing methods either optimize externa…
ToolRM: Towards Agentic Tool-Use Reward Modeling
Renhao Li, Jianhong Tu, Yang Su +6
Reward models (RMs) play a critical role in aligning large language models (LLMs) with human preferences. Yet in the domain of tool learning, the lack of RMs specifically designed…
HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation
Shijie Zhang, Renhao Li, Songsheng Wang +3
The advancement of Large Language Models (LLMs) enables flexible and interpretable automatic evaluations. In the field of machine translation evaluation, utilizing LLMs with transl…
Exploring the Impact of Personality Traits on LLM Bias and Toxicity
Shuo Wang, Renhao Li, Xi Chen +3
With the different roles that AI is expected to play in human life, imbuing large language models (LLMs) with different personalities has attracted increasing research interests. W…
AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling
Ancheng Xu, Di Yang, Renhao Li +13
Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automated counseling offers a potential…
CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling
Chenhao Zhang, Renhao Li, Minghuan Tan +7
Using large language models (LLMs) to assist psychological counseling is a significant but challenging task at present. Attempts have been made on improving empathetic conversation…
CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation
Renhao Li, Minghuan Tan, Derek F. Wong +1
In recent years, instruction fine-tuning (IFT) on large language models (LLMs) has garnered considerable attention to enhance model performance on unseen tasks. Attempts have been…