Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

• URL: http://arxiv.org/abs/2305.11595v3
• Date: Wed, 18 Oct 2023 06:32:15 GMT
• Title: Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate
• Authors: Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu and Bing Qin
• Abstract summary: Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues. To examine whether LLMs can collaborate effectively to achieve a consensus for a shared goal, we focus on commonsense reasoning. Our work contributes to understanding the inter-consistency among LLMs and lays the foundation for developing future collaboration methods.
• Score: 41.949869545423375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues. Existing works primarily focus on the inconsistency issues within a single LLM, while we complementarily explore the inter-consistency among multiple LLMs for collaboration. To examine whether LLMs can collaborate effectively to achieve a consensus for a shared goal, we focus on commonsense reasoning, and introduce a formal debate framework (FORD) to conduct a three-stage debate among LLMs with real-world scenarios alignment: fair debate, mismatched debate, and roundtable debate. Through extensive experiments on various datasets, LLMs can effectively collaborate to reach a consensus despite noticeable inter-inconsistencies, but imbalances in their abilities can lead to domination by superior LLMs. Leveraging a more advanced LLM like GPT-4 as an authoritative judge can boost collaboration performance. Our work contributes to understanding the inter-consistency among LLMs and lays the foundation for developing future collaboration methods. Codes and data are available at https://github.com/Waste-Wood/FORD

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