Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents
- URL: http://arxiv.org/abs/2502.20073v1
- Date: Thu, 27 Feb 2025 13:31:13 GMT
- Title: Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents
- Authors: Haochen Sun, Shuwen Zhang, Lei Ren, Hao Xu, Hao Fu, Caixia Yuan, Xiaojie Wang,
- Abstract summary: Large language models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks.<n>This paper proposes a new benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments.
- Score: 17.773801766612703
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks. This paper proposes a new LLM-powered Multi-Agent System (LLM-MAS) benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments. Collab-Overcooked extends existing benchmarks from two novel perspectives. First, it provides a multi-agent framework supporting diverse tasks and objectives and encourages collaboration through natural language communication. Second, it introduces a spectrum of process-oriented evaluation metrics to assess the fine-grained collaboration capabilities of different LLM agents, a dimension often overlooked in prior work. We conduct extensive experiments over 10 popular LLMs and show that, while the LLMs present a strong ability in goal interpretation, there is a significant discrepancy in active collaboration and continuous adaption that are critical for efficiently fulfilling complicated tasks. Notably, we highlight the strengths and weaknesses in LLM-MAS and provide insights for improving and evaluating LLM-MAS on a unified and open-sourced benchmark. Environments, 30 open-ended tasks, and an integrated evaluation package are now publicly available at https://github.com/YusaeMeow/Collab-Overcooked.
Related papers
- Teamwork makes the dream work: LLMs-Based Agents for GitHub README.MD Summarization [7.330697128881243]
We propose Metagente as a novel approach to amplify the synergy of various Large Language Models (LLMs)
Metagente is a Multi-Agent framework based on a series of LLMs to self-optimize the system through evaluation, feedback, and cooperation among specialized agents.
The performance gain compared to GitSum, the most relevant benchmark, ranges from 27.63% to 60.43%.
arXiv Detail & Related papers (2025-03-13T20:42:39Z) - MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning [26.736078756799635]
We introduce a new post-training paradigm MAPoRL (Multi-Agent Post-co-training for collaborative LLMs with Reinforcement Learning)<n>In MAPoRL, multiple LLMs first generate their own responses independently and engage in a multi-turn discussion to collaboratively improve the final answer.<n>A MAPoRL verifier evaluates both the answer and the discussion, by assigning a score that verifies the correctness of the answer.<n>The score serves as the co-training reward, and is then maximized through multi-agent RL.
arXiv Detail & Related papers (2025-02-25T18:33:48Z) - EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents [63.43699771428243]
EmbodiedBench is an extensive benchmark designed to evaluate vision-driven embodied agents.
We evaluated 19 leading proprietary and open-source MLLMs within EmbodiedBench.
MLLMs excel at high-level tasks but struggle with low-level manipulation.
arXiv Detail & Related papers (2025-02-13T18:11:34Z) - When One LLM Drools, Multi-LLM Collaboration Rules [98.71562711695991]
We argue for multi-LLM collaboration to better represent the extensive diversity of data, skills, and people.<n>We organize existing multi-LLM collaboration methods into a hierarchy, based on the level of access and information exchange.<n>We envision multi-LLM collaboration as an essential path toward compositional intelligence and collaborative AI development.
arXiv Detail & Related papers (2025-02-06T21:13:44Z) - LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions [8.55917897789612]
We focus on the cooperative tasks of multiple agents with a common goal and communication among them.
We also consider human-in/on-the-loop scenarios enabled by the language component in the framework.
arXiv Detail & Related papers (2024-05-17T22:10:23Z) - Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond [24.151927600694066]
Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs.
This paper conducts the first comprehensive experiment to investigate how far we have been in applying Large Language Models (LLMs) to generate high-quality commit messages.
arXiv Detail & Related papers (2024-04-23T08:24:43Z) - Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs.
We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer.
This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
arXiv Detail & Related papers (2024-01-14T16:17:07Z) - Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents.
Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes.
We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
arXiv Detail & Related papers (2023-09-30T07:11:39Z) - MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language
Feedback [78.60644407028022]
We introduce MINT, a benchmark that evaluates large language models' ability to solve tasks with multi-turn interactions.
LLMs generally benefit from tools and language feedback, with performance gains of 1-8% for each turn of tool use.
LLMs evaluated, supervised instruction-finetuning (SIFT) and reinforcement learning from human feedback (RLHF) generally hurt multi-turn capabilities.
arXiv Detail & Related papers (2023-09-19T15:25:42Z) - AgentBench: Evaluating LLMs as Agents [88.45506148281379]
Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks.
We present AgentBench, a benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
arXiv Detail & Related papers (2023-08-07T16:08:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.