Fusion-in-Decoder (FiD) is an effective retrieval-augmented language model applied across a variety of open-domain tasks, such as question answering, fact checking, etc. In FiD, supporting passages are first retrieved and then processed using a generative model (Reader), which can cause a significant bottleneck in decoding time, particularly with long outputs. In this work, we analyze the contribution and necessity of all the retrieved passages to the performance of reader models, and propose eliminating some of the retrieved information, at the token level, that might not contribute essential information to the answer generation process. We demonstrate that our method can reduce run-time by up to 62.2%, with only a 2% reduction in performance, and in some cases, even improve the performance results.

翻译：Fusion-in-Decoder (FiD) 是一种有效的检索增强型语言模型，广泛应用于开放域任务，如问答、事实验证等。在FiD模型中，首先检索支持性段落，然后使用生成模型（阅读器）进行处理，这会在解码阶段造成显著的瓶颈，尤其是在生成长文本时。在本研究中，我们分析了所有检索到的段落对阅读器模型性能的贡献和必要性，并提出在词元级别消除部分可能对答案生成过程不提供关键信息的检索内容。实验证明，我们的方法可将运行时间减少高达62.2%，且性能仅下降2%，在某些情况下甚至能提升性能结果。