Multi-LLM Text Summarization

• URL: http://arxiv.org/abs/2412.15487v1
• Date: Fri, 20 Dec 2024 01:55:26 GMT
• Title: Multi-LLM Text Summarization
• Authors: Jiangnan Fang, Cheng-Tse Liu, Jieun Kim, Yash Bhedaru, Ethan Liu, Nikhil Singh, Nedim Lipka, Puneet Mathur, Nesreen K. Ahmed, Franck Dernoncourt, Ryan A. Rossi, Hanieh Deilamsalehy,
• Abstract summary: We propose a Multi-LLM summarization framework, and investigate two different multi-LLM strategies including centralized and decentralized. Our framework has two fundamentally important steps at each round of conversation: generation and evaluation. We find that our multi-LLM summarization approaches significantly outperform the baselines that leverage only a single LLM by up to 3x.
• Score: 58.74987409988719
• License:
• Abstract: In this work, we propose a Multi-LLM summarization framework, and investigate two different multi-LLM strategies including centralized and decentralized. Our multi-LLM summarization framework has two fundamentally important steps at each round of conversation: generation and evaluation. These steps are different depending on whether our multi-LLM decentralized summarization is used or centralized. In both our multi-LLM decentralized and centralized strategies, we have k different LLMs that generate diverse summaries of the text. However, during evaluation, our multi-LLM centralized summarization approach leverages a single LLM to evaluate the summaries and select the best one whereas k LLMs are used for decentralized multi-LLM summarization. Overall, we find that our multi-LLM summarization approaches significantly outperform the baselines that leverage only a single LLM by up to 3x. These results indicate the effectiveness of multi-LLM approaches for summarization.

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