Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Solving Complex Science Problems

Core Concepts

A collaborative multi-agent, multi-reasoning-path prompting framework (CoMM) can significantly improve the reasoning capabilities of large language models in solving complex science problems.

Abstract

The content describes a novel prompting framework called CoMM that leverages multiple language model agents, each playing a different role (e.g., physicist, mathematician, summarizer), to collaboratively solve complex science problems. Key highlights: Large language models (LLMs) have shown great ability in solving traditional natural language tasks, but their reasoning capabilities are still limited for complicated science problems. The CoMM framework prompts LLMs to play different roles in a problem-solving team and encourages them to collaborate using different reasoning paths. Applying different reasoning paths for different roles is an effective strategy to implement few-shot prompting approaches in the multi-agent scenarios. Empirical results demonstrate the effectiveness of CoMM on two college-level science problems, outperforming competitive baselines. Further analysis shows the necessity of prompting LLMs to play different roles or experts independently, rather than a single agent playing multiple roles.

Stats

The separation of the bright fringes is 1.0 millimeter. The separation of the slits is 0.5 micrometers. Doubling the frequency of the laser light means halving the wavelength.

Quotes

"Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. However, their ability is still limited in solving complicated science problems." "Empirical results demonstrate the effectiveness of the proposed methods on two college-level science problems over competitive baselines." "Our further analysis shows the necessity of prompting LLMs to play different roles or experts independently."

Key Insights Distilled From

CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving

by Pei Chen,Bor... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.17729.pdf

Deeper Inquiries

How can the CoMM framework be extended to handle a wider range of complex problems beyond science, such as in the social sciences or humanities?

The CoMM framework can be extended to handle a wider range of complex problems beyond science by adapting the roles of the agents and the reasoning paths to suit the specific requirements of social sciences or humanities tasks. For social science problems, agents could be prompted to play roles such as sociologists, psychologists, or ethicists, each bringing their domain expertise to the problem-solving process. The reasoning paths could be tailored to incorporate social theories, ethical considerations, or psychological perspectives. By customizing the roles and reasoning paths to align with the nuances of social science or humanities problems, the CoMM framework can effectively address a broader spectrum of complex issues.

What are the potential limitations or drawbacks of the multi-agent, multi-reasoning-path approach compared to a single-agent approach, and how can they be addressed?

One potential limitation of the multi-agent, multi-reasoning-path approach compared to a single-agent approach is the increased complexity and coordination required among multiple agents. Coordinating the interactions and reasoning paths of multiple agents may introduce challenges such as communication overhead, conflicting perspectives, or difficulty in reaching a consensus. To address these limitations, clear guidelines and protocols for communication and collaboration among agents can be established. Additionally, regular feedback loops and mechanisms for resolving conflicts or discrepancies in reasoning can be implemented to ensure smooth coordination among agents. Training the agents on diverse datasets and scenarios can also help enhance their adaptability and collaborative problem-solving skills.

How can the automatic prompting design for the CoMM framework be improved to make it more generalizable and less task-specific?

To improve the automatic prompting design for the CoMM framework and make it more generalizable and less task-specific, several strategies can be implemented. Firstly, developing a more flexible and adaptive prompting mechanism that can dynamically adjust to different problem domains and scenarios can enhance the framework's generalizability. This could involve incorporating reinforcement learning techniques to optimize the prompting process based on the agents' performance and feedback. Secondly, leveraging transfer learning approaches to pre-train the agents on a diverse range of tasks and domains can help them generalize better to new problem sets. Additionally, incorporating meta-learning techniques to enable the agents to quickly adapt to new tasks with minimal task-specific training data can enhance the framework's versatility and applicability across various domains.