This work introduces AnchorQE, a training-free method that separately encodes the original query and its expansion before interpolating them, and shows that AnchorQE improves retrieval effectiveness by up to 12.89% when compared to widely-used expansion-only or text-level concatenation baselines across TREC-DL, LoTTE, and BEIR.
Abstract
Large language models (LLMs) can generate query expansions without task-specific training, yet the same expansions often make a frozen dense retriever worse. We identify an underexplored factor: prior work has often focused on what text is generated, while how generated text is incorporated into dense retrievers has received less systematic attention. By holding generated expansions fixed, we show that performance degradation can often be attributed to the integration method itself. We introduce AnchorQE, a training-free method that separately encodes the original query and its expansion before interpolating them. The interpolation factor is estimated using an unsupervised online strategy that operates over a small part of the unlabeled test stream. Intuitively, our strategy assigns high expansion trust only when expansions are both retrieval-strong and consistent with the original query's retrieved evidence. We show that AnchorQE improves retrieval effectiveness by up to 12.89% when compared to widely-used expansion-only or text-level concatenation baselines across TREC-DL, LoTTE, and BEIR. Further, we show that our online strategy to estimate the interpolation factor outperforms a fixed weight tuned on a development partition by up to 3.81%.
LLM-based query expansion improves retrieval by generating document-like passages. In hybrid retrieval, however, most evaluations fuse fixed top-$L$ dense and sparse rankings. Because the cutoff controls both which cross-channel contributions enter fusion and how much of each ranking is accessed, gains measured at one $L$ can change or reverse at another. We separate these effects by evaluating retrieval effectiveness under complete-list fusion and recording the policy-specific per-channel replay stopping depths at which its ordered top-$K$ is certified. We then introduce DESA (Dense Expansion and Sparse Anchoring), a channel-asymmetric query expansion method. An LLM generates complementary reference passages; orthogonal residual expansion adds their new semantic directions to the dense query, while score-product anchoring incorporates their lexical cues into sparse retrieval without broadening the original query's lexical support. 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 access depths by 36.90% and 36.56%. With equal dataset weighting, 63.31% of queries become shallower in both channels. However, both depths increase with Contriever on Touch\'e-2020. These results support channel-specific integration of generated passages and joint evaluation of retrieval effectiveness and access depth.
Large language models (LLMs) can both expand underspecified queries and encode text as dense representations, suggesting a unified model for query expansion and retrieval. Existing systems usually rely on prompted expansions, independently trained modules, or staged optimization, leaving generated expansions only indirectly aligned with the retrieval loss that judges them. We train a single decoder-only LLM end to end, where the same model generates the expansion and encodes both the expanded query and candidate documents. This unified setting creates a moving-target problem: retrieval supervision should improve query-side expansion, but the same update also shifts the document embeddings that serve as retrieval targets. We introduce Document Embedding Preservation Tuning (DEPT), which keeps tuned document embeddings close to cached initial embeddings while allowing retrieval gradients to pass through straight-through decoding into the generator. DEPT converts joint query--document movement into query-side adaptation against approximately stable, whitened document embeddings that support index reuse and online hard-negative mining. Experiments with Qwen3-4B-Instruct-2507 and LLaMA-3.2-3B-Instruct on five datasets in BEIR benchmark show that DEPT improves average retrieval quality over training-free, independently trained, and staged unified baselines, while ablations isolate the effects of preservation, whitening, end-to-end expansion training, and online negatives. Code is available at https://github.com/ILSparkle/DEPT.
Jingyuan Wang, Richong Zhang, Zhijie Nie et al.· 0 citations
Advancements in retrieval models necessitate re-indexing, a computationally expensive process for large-scale production environments. While updating only the query encoder and continuing to use the old index could be a promising middle ground, dense retrieval systems suffer severe performance drops in this setting. We investigate whether Learned Sparse Retrieval (LSR) can mitigate this backward compatibility issue, as its lexical matching may provide a stable term-based anchor to preserve compatibility across model versions. Experiments on BEIR and the streaming settings of LoTTE show that upgrading only the query encoder causes only a small effectiveness drop in LSR when no mitigation applied, whereas dense retrieval fails severely. We explore lightweight query adaptation methods including ranking fusion, representation fusion, and minimal-training adapters to further improve compatibility. These approaches significantly improve backward compatibility on BEIR and effectively reduce performance loss in streaming retrieval. Code: https://github.com/JingfenQiao/LSR-BC.git
Jingfen Qiao, Gabrielle Poerwawinata, Thong Nguyen et al.· Annual International ACM SIG...· 0 citations
The Adaptive Multi-Stage Vector Retrieval (AMSVR) framework is proposed, prioritising weighted, drift-resistant composition over uniform fusion, and offers tailored configurations: AMSVR-Scientific (dense + tuned hybrid) peaks at NDCG@10 = 0.7570 on SciFact, while AMSVR-Full (seven stages) targets broader, noisier corpora where Recall@100 matters most.
Samsudeen Alabi Bankole, Yakub Kayode Saheed· NLP & Big Data· 0 citations
Prior work has shown that vector-based pseudo relevance feedback (PRF) is an effective technique for query expansion for improving retrieval results in dense information retrieval. In dense retrieval, ColBERT-PRF has emerged as a novel mechanism, using cluster centroids built from feedback documents as PRF expansion tokens and leveraging statistical information from the closest neighboring token ids to dictate how useful these expansion tokens are. While this approach has been shown to work well in the monolingual retrieval setting for English using the original ColBERT infrastructure, such systems have since evolved to improve inference speed, reduce storage and memory usage, and support cross-language (CLIR) and multilingual (MLIR) retrieval. As a result, many of these advancements have reduced the ability to utilize token-level statistics. In this work, we aim to explore how well this type of approach can adapt to dense retrieval models when it is not feasible to use surface-form information to pick discriminating expansion tokens. Furthermore, we explore alternative clustering mechanisms, such as HDBScan, to compare how different clustering methods perform at building clusters that can be useful for PRF. Experiments on MLIR, CLIR, and Report Generation tasks, such as those in the TREC 2024 NeuCLIR Report Generation Pilot Task, show that even without access to these token statistics, the use of cluster centroids for PRF can still improve nDCG and α-nDCG by up to 12%.
X. Velez, Andrew Yates, Eugene Yang et al.· Annual International ACM SIG...· 0 citations
LLM-based chatbots are increasingly augmented with retrieval mechanisms operating over web-scale corpora. Evaluating the effectiveness of these retrieval components is challenging, as explicit relevance judgments are often unavailable. Query performance prediction (QPP) addresses this limitation by providing unsupervised estimates of retrieval effectiveness. However, existing QPP methods assume a static corpus and do not account for the impact of corpus growth on query performance. In this work, we extend the QPP paradigm by studying query performance degradation under corpus inflation in dense retrieval systems. Using tiered corpora with fixed relevance judgments, we analyze how query effectiveness evolves as the corpus (index) size increases and evaluate the ability of established score-based and embedding-based post-retrieval QPP methods to predict such degradation. Our findings show that the reliability of these predictors is dependent on the dataset. We propose simple adaptations to established QPP measures, most notably a top-k vs background Wasserstein distance measure, which yield more consistent associations with degradation and outperform their original counterparts. These findings highlight limitations of several QPP approaches in large-scale, continuously expanding retrieval environments and motivate the development of corpus-growth-aware QPP measures.
Kanishka Ghosh Dastidar, M. Dinzinger, Laura Caspari et al.· Annual International ACM SIG...· 0 citations