Multi-LLM Text Summarization
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.
676984a1edea1efd81017959
Jiangnan Fang
676984a1edea1efd8101795a
Cheng-Tse Liu
676984a1edea1efd8101795b
Jieun Kim
676984a1edea1efd8101795c
Yash Bhedaru
676984a1edea1efd8101795d
Ethan Liu
676984a1edea1efd8101795e
Nikhil Singh
676984a1edea1efd8101795f
Nedim Lipka
676984a1edea1efd81017960
Puneet Mathur
676984a1edea1efd81017961
Nesreen K. Ahmed
claimed_verified
2025-01-07T08:42:50.165Z
663e588ccca86c8371a913d9
https://cdn-avatars.huggingface.co/v1/production/uploads/663e588ccca86c8371a913d9/wgVGw5U77cqwtjovhsXyR.jpeg
Nesreen Ahmed
user
nkahmed
676984a1edea1efd81017962
Franck Dernoncourt
claimed_verified
2024-12-30T19:36:09.059Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
676984a1edea1efd81017963
Ryan A. Rossi
676984a1edea1efd81017964
Hanieh Deilamsalehy
Multi-LLM Text Summarization
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.
A framework for multi-LLM summarization using both centralized and decentralized strategies improves performance over single-LLM models.
multi-LLM centralized decentralized generation evaluation diverse summaries