Query Variant Detection Using Retriever as Environment (2025)

Abstract: This paper addresses the challenge of detecting query variants—pairs of queries with identical intents. One application in commercial search engines is reformulating user queries with its variant online. While measuring pairwise query similarity has been an established standard, it often falls short of capturing semantic equivalence when word forms or order differ. We propose leveraging the retrieval as an environment feedback (EF), based on the premise that desirable retrieval outcomes from equivalent queries should be interchangeable. Experimental results on both proprietary and public datasets demonstrate the efficacy of the proposed method, both with and without LLM calls.

## introduction identifying query variants-semantically equivalent queries-is critical for ensuring search engines consistently return identical results for queries that reflect the same intent. one application of this detection is query reformulation, where a user query q is augmented or replaced with its variant q foot_0 to improve quality and consistency in retrieval results. however, identifying query variants is nontrivial as a highly similar query pair, often relying heavily on lexical similarity between q and q 1 , may fail to differ in word form, order, or phrasing despite sharing the same intent @xcite . when latency requirements are relaxed, large language models (llms) may offer an improved semantic understanding @xcite , and have been used for query understanding related tasks such as classifying search intent @xcite . llms have the advantage of observing query variants in diverse surrounding contexts ˚equal contribution. : correspondence to: seungwonh@snu.ac.kr. our work demonstrates how leveraging the retriever as an environment feedback (ef) enhances query variant detection across diverse scenarios. ef utilizes retrieval results as additional features-by quantifying query-document or document-document similarity-beyond traditional pairwise query similarity. for instance, retrieval results for query variants exhibit high similarity @xcite . specifically, we show these additional ef features improve performance in both latency-sensitive cases (by training an efficient classifier) and latency-relaxed cases (by integrating with llms). our generalized approach naturally supports public data with limited training annotations, or weaker ef as well. our contributions are as follows: ## related work this section overviews the task of query variant identification (section 2.1), and relevant literature on utilizing the environment feedback from retriever (section 2.2). ## preliminaries we first provide the task formulation and basic notation to be used for the rest of the paper. ## method we first discuss the baseline of training the verifier f in a supervised fashion according to eq. 4, utilizing the queries q and q 1 only, in section 4.1. then, in section 4.2, we explain how we designed and trained our efficient verifier f , incorporating ef signals. finally, we explain how such a system can be scaled in section 4.3. ## conclusion in this paper, we explored the use of ef to identify query variants. our findings demonstrate that our approach substantially outperforms deployed baselines, in both budget-constrained and less restricted scenarios. in addition, we release the expert annotations to support future development in this area. prompt for query rewriting for benchmark preprocessing given a question in its natural sentence form, convert it into a more concise format that is more likely to be issued as a search query to search engines. the search intent of the user must be preserved. as in the following examples, decide whether the two given queries are equivalent or not. here is the question in sentence form, convert it to concise form that is more likely to be a real search query. question: {query (q)} answer: the equivalent query condition requires that both queries have the same search intent, and that if the same search result is presented to the user for both queries, the user's satisfaction level should be the same as well. as in the following examples, decide whether the two given queries are equivalent or not. your final answer should be either 'yes' or 'no'. here are the two queries to be tested for equivalence: query 1: {query 1 (q)} query 2: {query 2 (q 1 )} answer: