2 citations · 2 across the 8 of their papers we have counts for
4 papers · 2 filters
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…