LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

• URL: http://arxiv.org/abs/2306.02561v3
• Date: Fri, 30 Jun 2023 21:39:54 GMT
• Title: LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion
• Authors: Dongfu Jiang, Xiang Ren, Bill Yuchen Lin
• Abstract summary: Our framework consists of two modules: PairRanker and GenFuser. PairRanker employs a specialized pairwise comparison method to distinguish subtle differences between candidate outputs. GenFuser aims to merge the top-ranked candidates, generating an improved output.
• Score: 33.73671362609599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present LLM-Blender, an ensembling framework designed to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). Our framework consists of two modules: PairRanker and GenFuser, addressing the observation that optimal LLMs for different examples can significantly vary. PairRanker employs a specialized pairwise comparison method to distinguish subtle differences between candidate outputs. It jointly encodes the input text and a pair of candidates, using cross-attention encoders to determine the superior one. Our results demonstrate that PairRanker exhibits the highest correlation with ChatGPT-based ranking. Then, GenFuser aims to merge the top-ranked candidates, generating an improved output by capitalizing on their strengths and mitigating their weaknesses. To facilitate large-scale evaluation, we introduce a benchmark dataset, MixInstruct, which is a mixture of multiple instruction datasets featuring oracle pairwise comparisons. Our LLM-Blender significantly outperform individual LLMs and baseline methods across various metrics, establishing a substantial performance gap.

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