Generated Query Expansion Still Helps Strong Sparse Retrieval: A Controlled Study with SPLADE-v3
Authors: Ryan C. Barron, Cade W. Trotter, Maksim E. Eren, Kim Ø. Rasmussen, Liz D. Miller, Benjamin J. Migliori
Organizations: Computational Intelligence & Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. · Modeling and Observations of Earth Systems, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. · Fluid Dynamics and Solid Mechanics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. · Intelligence & Systems Analysis, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. · Advanced Research in Cyber Systems, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
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 the four generated formats of term lists, a pseudo-document, multiple pseudo-references, and corpus-steered text all together 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.
Figures & tables
NFCorpus
TREC-COVID
SciDocs
Method
nDCG@10
R@100
nDCG@10
R@100
nDCG@10
R@100
BM25
0.3231
0.2457
0.5696
0.1091
0.1490
0.3477
RM3
0.3465
0.3229
0.5635
0.1168
0.1491
0.3620
TAS-B dense
0.2755
0.2487
0.3087
0.0335
0.1406
0.3222
BM25 + dense RRF
0.3342
0.2819
0.5511
0.0894
0.1690
0.3848
ColBERTv2
0.3392
0.2803
0.7100
0.1307
0.1482
0.3543
TABLE I: First-stage effectiveness, with nDCG@10 as the primary metric, R@100 as deeper coverage, and bold marking the best value in each dataset column.
Dataset
Method
nDCG@10
MRR@10
MAP@10
P@10
R@10
R@100
Cand. recall
NFCorpus
CSQE
+4.81
+5.10
+5.30
+4.45
+1.81
+6.20
+6.28
TREC-COVID
Query2doc
+8.92
+3.74
+6.48
+3.91
+6.99
+10.44
+5.11
SciDocs
CSQE
+9.47
+9.16
+12.58
+7.38
+7.39
+5.57
+3.14
TABLE II: Relative improvement (%) over SPLADE-v3 for the strongest generated nDCG@10 condition on each collection.
Dataset
Method
Δ
95% CI
padj
dz
W/T/L
NFCorpus
CSQE
+0.0173
[+0.0106, +0.0241]
4e-05
+0.28
99/175/49
NFCorpus
HiQE , ungated
+0.0013
[-0.0007, +0.0034]
0.661
+0.07
40/246/37
TREC-COVID
Query2doc
+0.0649
[+0.0273, +0.1030]
0.00540
+0.47
34/5/11
TREC-COVID
HiQE , ungated
-0.0126
[-0.0259, +0.0007]
0.207
-0.26
16/12/22
SciDocs
CSQE
+0.0150
[+0.0111, +0.0189]
4e-05
+0.24
262/611/127
SciDocs
HiQE , ungated
+0.0006
[-0.0011, +0.0022]
1.000
+0.02
77/843/80
TABLE III: Compact paired per-query nDCG@10 analysis comparing, for each collection, the strongest aggregate generated condition and ungated HiQE with SPLADE-v3.
Method
Positive α
BOW Δ
Shuffle Δ
NFCorpus
Flat LLM-QE
8/10
+.0094
+.0104
Query2doc
8/9
+.0107
+.0141
MuGI-style
9/9
+.0085
+.0104
CSQE
10/10
+.0180
+.0167
TREC-COVID
TABLE IV: Interpolation robustness and same-content controls. “Positive α ” counts tested weights above SPLADE-v3. BOW and Shuffle are absolute Δ nDCG@10 at the configured α .
Variant
NFCorpus
TREC-COVID
SciDocs
Mean Δ
Random count-matched
−.0001
−.0060
+.0003
−.0020
Flat concepts, equal scores
−.0048
−.0223
−.0002
−.0091
No corpus validation
+.0006
−.0256
+.0006
−.0081
Equal relation weights
+.0005
−.0192
+.0014
−.0058
Parent only
+.0002
−.0054
+.0005
−.0016
Child only
+.0006
−.0093
+.0004
−.0028
TABLE V: Hierarchy ablations as absolute Δ nDCG@10 relative to SPLADE-v3, with mean Δ defined as the arithmetic mean across the three collections.
LLM-based query expansion improves retrieval by enriching the original query with additional context. Yet most methods remain generation-driven, producing plausible pseudo-documents or expansions without checking how the target corpus responds. This can introduce retrieval drift, amplify misleading vocabulary, or miss terms that distinguish relevant from non-relevant documents. We argue that effective expansion requires retrieval-grounded feedback, not just single-pass generation or unverified iteration. We introduce ADORE (ADapt, Observe, Relevance Evaluate), an iterative framework that turns retrieval outcomes into feedback for the next expansion. At each round, an LLM generates pseudo-passages, a retriever exposes the corpus response, and a relevance assessor evaluates retrieved documents against the original query. These judgments identify what to reinforce, what remains undercovered, and what to suppress. Across TREC Deep Learning, BEIR, and BRIGHT, ADORE consistently outperforms strong query expansion baselines with notable improvements across nearly all evaluation settings, improving average nDCG@10 by 24.5% over BM25 and 3.6% over the strongest prior query expansion method on BEIR, and by 122.9% over BM25 and 9.2% over the best query expansion baseline on BRIGHT. Our code and data are publicly available.
Amin Bigdeli, Negar Arabzadeh, Radin Hamidi Rad +3
University of Waterloo · University of California, Berkeley · Mila – Quebec AI Institute +1
Retrieval-augmented generation (RAG) systems rely on retrieval modules to ground large language model (LLM) outputs. LLM-based query expansion enriches retrieval with document-like passages, but evaluations of hybrid retrieval often fuse fixed top-L prefixes of dense and sparse rankings. Because L controls cross-channel contributions and ranking access, it can alter measured expansion gains. We therefore evaluate complete-list effectiveness and record per-channel replay stopping depths required to certify the ordered top-K. This changes the design: because both rankings determine the fused result, their query constructions should be coordinated rather than designed independently. We present DESA (Dense Expansion and Sparse Anchoring), which shares generated references across channels but specializes their integration. Orthogonal residual expansion adds new semantic directions to the dense query, whereas score-product anchoring reorders the original sparse support without admitting expansion-only matches. The same references thus play complementary roles: Dense expands; Sparse anchors. Across seven BEIR datasets, DESA improves nDCG@10 and Recall@20 over the unexpanded query by 3.82% and 2.38%, while reducing dense and sparse replay stopping depths by 36.90% and 36.56%.
Chunran Zhang
School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu, China
We propose Latent Terms, a method revealing that models trained for dense retrieval, whether single- or multi-vector, learn representations that can trivially be decomposed into retrieval-ready sparse features. When trained on frozen retrievers, Sparse Autoencoders without any retrieval-specific adjustments extract a latent vocabulary with approximately Zipfian collection statistics, directly suitable for classical sparse retrieval scoring via BM25. This approach enables sparse retrieval while requiring no learned expansion objective or sparse retrieval supervision whatsoever, and can be readily applied to any dense retriever. Latent Terms is able to match or outperform single-vector scoring methods from its own base model as well as comparable SPLADE variants. In addition, it substantially outperforms its base model on LIMIT, a task specifically designed to highlight the failures of single-vector retrieval. Overall, our results highlight that neural retrievers contain more expressive and indexable structure than their default scoring functions expose, but that other methods can nonetheless be leveraged.
Benjamin Clavié, Sean Lee, Aamir Shakir +1
Mixedbread AI · National Institute of Informatics (NII) · University of Tsukuba