LLM-Blender：利用成对排名和生成融合对大型语言模型进行集成

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

June 5, 2023

作者: Dongfu Jiang, Xiang Ren, Bill Yuchen Lin

cs.AI

摘要

我们提出了LLM-Blender，这是一个集成框架，旨在通过利用多个开源大型语言模型（LLMs）的多样优势，实现始终优越的性能。我们的框架包括两个模块：PairRanker和GenFuser，解决了不同示例的最佳LLMs可能显著变化的观察。PairRanker采用专门的成对比较方法来区分候选输出之间的细微差异。它联合编码输入文本和一对候选项，使用交叉注意力编码器来确定哪个更优。我们的结果表明，PairRanker与基于ChatGPT的排名具有最高的相关性。然后，GenFuser旨在合并排名靠前的候选项，通过利用它们的优势并减轻它们的弱点，生成一个改进的输出。为了促进大规模评估，我们引入了一个基准数据集MixInstruct，这是多个指令数据集的混合，具有oracle成对比较。我们的LLM-Blender在各种指标上明显优于单个LLMs和基准方法，确立了实质性的性能差距。

English

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.