Seminar: "ChatArena: Multi-Agent Language Game Environments for Large Language Models"
Abstract
Recent development of Large Language Models (LLMs) unveils exciting and uncharted territory of multi-agent interactions between multiple LLMs. Our focus will be on how multiple LLMs can collaborate and compete within complex scenarios and games. To study this, we have crafted ChatArena, an innovative library that fosters the creation of multi-agent language game environments and encourages research on the autonomous behaviour and social interaction of LLM agents. The talk will elucidate the core features of ChatArena, such as its flexible framework for defining multiple players and environments, built on the Markov Decision Process, a collection of language game environments for understanding and benchmarking agent LLMs, and its user-friendly interfaces, including Web UI and CLI for developing and engineering LLM agents. We welcome you to discover more about our project and to engage with a live demonstration at http://chatarena.org and http://demo.chatarena.org, respectively.
Invited Speaker: Yuxiang Wu (University College London)
Bio: Yuxiang Wu is a final-year PhD student in the Department of Computer Science at University College London (UCL), advised by Prof. Sebastian Riedel and Prof. Pontus Stenetorp. His research mainly focuses on Large Language Models, Question Answering, and Knowledge-augmented Pre-trained Language Models. He has published 12 papers in top AI/NLP conferences and journals (ACL, EMNLP, AAAI, IJCAI, KDD, TACL). His works have been cited by more than 1900 times, and won Best Paper Award AKBC2020, champion of Neurips 2020 EfficientQA Competition, and Best Poster Award ENLSP Workshop in NeurIPS 2022.
