Performing automatic reformulations of a user's query is a popular paradigm used in information retrieval (IR) for improving effectiveness -- as exemplified by the pseudo-relevance feedback approaches, which expand the query in order to alleviate the vocabulary mismatch problem. Recent advancements in generative language models have demonstrated their ability in generating responses that are relevant to a given prompt. In light of this success, we seek to study the capacity of such models to perform query reformulation and how they compare with long-standing query reformulation methods that use pseudo-relevance feedback. In particular, we investigate two representative query reformulation frameworks, GenQR and GenPRF. GenQR directly reformulates the user's input query, while GenPRF provides additional context for the query by making use of pseudo-relevance feedback information. For each reformulation method, we leverage different techniques, including fine-tuning and direct prompting, to harness the knowledge of language models. The reformulated queries produced by the generative models are demonstrated to markedly benefit the effectiveness of a state-of-the-art retrieval pipeline on four TREC test collections (varying from TREC 2004 Robust to the TREC 2019 Deep Learning). Furthermore, our results indicate that our studied generative models can outperform various statistical query expansion approaches while remaining comparable to other existing complex neural query reformulation models, with the added benefit of being simpler to implement.

翻译：对用户查询进行自动改写是信息检索中提升效果的流行范式——以伪相关反馈方法为例，该方法通过扩展查询来缓解词汇不匹配问题。生成式语言模型的最新进展已证明其能够生成与给定提示相关的回复。基于这一成功，我们旨在研究此类模型执行查询重构的能力，并将其与使用伪相关反馈的长期查询重构方法进行对比。具体而言，我们研究了两种代表性的查询重构框架：GenQR和GenPRF。GenQR直接重构用户的输入查询，而GenPRF通过利用伪相关反馈信息为查询提供额外上下文。对于每种重构方法，我们采用了包括微调和直接提示在内的不同技术，以利用语言模型的知识。实验表明，生成模型产生的重构查询显著提升了最先进检索流水线在四个TREC测试集（涵盖从TREC 2004 Robust到TREC 2019 Deep Learning）上的效果。此外，我们的结果表明，所研究的生成模型能够超越多种统计查询扩展方法，同时与其他现有复杂神经查询重构模型效果相当，且实现更为简单。