6 papers
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
Multi-Agent Evolve: LLM Self-Improve through Co-evolution
Yixing Chen, Yiding Wang, Siqi Zhu +5
Reinforcement Learning (RL) has demonstrated significant potential in enhancing the reasoning capabilities of large language models (LLMs). However, the success of RL for LLMs heav…
Law in Silico: Simulating Legal Society with LLM-Based Agents
Yiding Wang, Yuxuan Chen, Fanxu Meng +3
Since real-world legal experiments are often costly or infeasible, simulating legal societies with Artificial Intelligence (AI) systems provides an effective alternative for verify…
Beyond Outcome Reward: Decoupling Search and Answering Improves LLM Agents
Yiding Wang, Zhepei Wei, Xinyu Zhu +1
Enabling large language models (LLMs) to utilize search tools offers a promising path to overcoming fundamental limitations such as knowledge cutoffs and hallucinations. Recent wor…
HD-PiSSA: High-Rank Distributed Orthogonal Adaptation
Yiding Wang, Fauxu Meng, Xuefeng Zhang +3
Existing parameter-efficient fine-tuning (PEFT) methods for large language models (LLMs), such as LoRA and PiSSA, constrain model updates to low-rank subspaces, limiting their expr…
Simulating Human-like Daily Activities with Desire-driven Autonomy
Yiding Wang, Yuxuan Chen, Fangwei Zhong +2
Desires motivate humans to interact autonomously with the complex world. In contrast, current AI agents require explicit task specifications, such as instructions or reward functio…