Bridging the Gap between Different Vocabularies for LLM Ensemble

Yangyifan Xu, Jinliang Lu, Jiajun Zhang


Abstract
Ensembling different large language models (LLMs) to unleash their complementary potential and harness their individual strengths is highly valuable. Nevertheless, vocabulary discrepancies among various LLMs have constrained previous studies to either selecting or blending completely generated outputs. This limitation hinders the dynamic correction and enhancement of outputs during the generation process, resulting in a limited capacity for effective ensemble. To address this issue, we propose a novel method to Ensemble LLMs via Vocabulary Alignment (EVA). EVA bridges the lexical gap among various LLMs, enabling meticulous ensemble at each generation step. Specifically, we first learn mappings between the vocabularies of different LLMs with the assistance of overlapping tokens. Subsequently, these mappings are employed to project output distributions of LLMs into a unified space, facilitating a fine-grained ensemble. Finally, we design a filtering strategy to exclude models that generate unfaithful tokens. Experimental results on commonsense reasoning, arithmetic reasoning, machine translation, and data-to-text generation tasks demonstrate the superiority of our approach compared with individual LLMs and previous ensemble methods conducted on complete outputs. Further analyses confirm that our approach can leverage knowledge from different language models and yield consistent improvement.
Anthology ID:
2024.naacl-long.395
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7133–7145
Language:
URL:
https://aclanthology.org/2024.naacl-long.395
DOI:
Bibkey:
Cite (ACL):
Yangyifan Xu, Jinliang Lu, and Jiajun Zhang. 2024. Bridging the Gap between Different Vocabularies for LLM Ensemble. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7133–7145, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Bridging the Gap between Different Vocabularies for LLM Ensemble (Xu et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.395.pdf
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