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

We present LLM-BL E N D E R, 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 GEN-FUSER, 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 largescale evaluation, we introduce a benchmark dataset, MixInstruct, which is a mixture of multiple instruction datasets featuring oracle pairwise comparisons. Our LLM-BL E N D E R significantly outperform individual LLMs and baseline methods across various metrics, establishing a substantial performance gap.