Query expansion is an effective approach for mitigating vocabulary mismatch
between queries and documents in information retrieval. One recent line of
research uses language models to generate query-related contexts for expansion.
Along this line, we argue that expansion terms from these contexts should
balance two key aspects: diversity and relevance. The obvious way to increase
diversity is to sample multiple contexts from the language model. However, this
comes at the cost of relevance, because there is a well-known tendency of
models to hallucinate incorrect or irrelevant contexts. To balance these two
considerations, we propose a combination of an effective filtering strategy and
fusion of the retrieved documents based on the generation probability of each
context. Our lexical matching based approach achieves a similar top-5/top-20
retrieval accuracy and higher top-100 accuracy compared with the
well-established dense retrieval model DPR, while reducing the index size by
more than 96%. For end-to-end QA, the reader model also benefits from our
method and achieves the highest Exact-Match score against several competitive
baselines.

本文提出了一种结合了有效过滤策略和基于上下文生成概率的检索文档融合的词汇匹配的方法，在减小索引大小的同时，提高了检索准确性；同时，在 end-to-end QA 模型中，该方法也能取得最高的 Exact-Match 分数。