Generated Query Expansion Still Helps Strong Sparse Retrieval: A Controlled Study with SPLADE-v3

Abstract

Scientific queries are often brief, while relevant papers use specialized vocabulary. Generated query expansion can bridge this mismatch, but earlier work suggests that its value shrinks as the underlying retriever becomes stronger. We test four generated formats—term lists, a pseudo-document, multiple pseudo-references, and corpus-steered text—with SPLADE-v3 on NFCorpus, TREC-COVID, and SciDocs. Every condition searches the same frozen document index and follows the same query-side integration rule and 256-dimension budget, isolating the effect of the added content. All twelve method–collection comparisons improve aggregate nDCG@10, with best relative gains of 4.81%, 8.92%, and 9.47%; eleven remain significant after Holm correction. The gain persists in 103 of 114 interpolation settings, including every setting that assigns at least 30% of the mixture weight to the original query. Shuffled-text and non-contextual lexical-bag controls also remain above baseline in all 24 aggregate comparisons, showing that the added vocabulary carries most of the benefit. A corpus-induced typed concept graph, by contrast, produces no consistent gain, and its relation, depth, validation, random, and gating controls do not rescue it. Generated vocabulary can therefore complement a strong learned sparse retriever, provided that the original query remains strongly represented.

Maksim E. Eren

Scientist

Maksim E. Eren is a Scientist at Los Alamos National Laboratory, specializing in machine learning and artificial intelligence for large-scale data science applications.