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Cool-Fusion: Fuse Large Language Models without Training

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arxiv 2407.19807 v2 pith:TZVVFFCH submitted 2024-07-29 cs.CL

Cong Liu , Xiaojun Quan , Yan Pan , Liang Lin , Weigang Wu , Xu Chen This is my paper
classification cs.CL
keywords llmscool-fusionsourcedifferentensemblelanguagelargemodels
verification ladder T0 review T1 audit T2 compute T3 formal
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We focus on the problem of fusing two or more heterogeneous large language models (LLMs) to leverage their complementary strengths. One of the challenges of model fusion is high computational load, specifically in fine-tuning or aligning vocabularies. To address this, we propose Cool-Fusion, a simple yet effective approach that fuses the knowledge of source LLMs, which does not require training. Unlike ensemble methods, Cool-Fusion is applicable to any set of source LLMs that have different vocabularies. To overcome the vocabulary discrepancies among LLMs, we ensemble LLMs on text level, allowing them to rerank the generated texts by each other with different granularities. Extensive experiments have been conducted across a variety of benchmark datasets. On GSM8K, Cool-Fusion increases accuracy from three strong source LLMs by a significant margin of 17.4\%.

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Cited by 3 Pith papers

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