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- W4385571189 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." @default.
- W4385571189 created "2023-08-05" @default.
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- W4385571189 date "2023-01-01" @default.
- W4385571189 modified "2023-10-16" @default.
- W4385571189 title "LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion" @default.
- W4385571189 doi "https://doi.org/10.18653/v1/2023.acl-long.792" @default.
- W4385571189 hasPublicationYear "2023" @default.
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