Jul 2026· Annual International ACM SIGIR Conference on Research and Development in Information Retrieval· pp. 3733-3737· 0 citations· 22 references
Computer Science
TL;DR
This work extends the QPP paradigm by studying query performance degradation under corpus inflation in dense retrieval systems and proposes 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.
Abstract
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.
Dense retrieval is commonly evaluated on benchmarks that represent each document with a single embedding, even though real-world retrieval systems often index long documents that require chunking. In these settings, the chosen chunking method not only affects retrieval quality, but also indexing throughput, query latency, and memory usage. Prior comparisons of chunking strategies have mainly focused on retrieval performance, leaving operational trade-offs underexplored. To address these issues, we evaluate eight representative chunking strategies across two scalable corpora, three embedding models, and multiple corpus sizes, measuring both retrieval effectiveness and system-level costs. Our results show that computationally expensive methods rarely provide consistent gains over simpler chunking. Instead, the best performing strategy depends on the embedding model, dataset, corpus size, and target retrieval metric. Methods with similar performance can also differ substantially in operational cost, showing that chunking should be seen as a multi-objective design decision.
Laura Caspari, K. G. Dastidar, M. Dinzinger et al.· 0 citations
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.
Retrieving relevant evidence from noisy web data is challenging, particularly in sensitive domains containing incomplete reports, heterogeneous language, and irrelevant content. We present Guardian Crawler, a reproducible retrieval-first testbed for controlled experiments on knowledge discovery and evidence-grounded summarization over synthetic web-like corpora. The architecture combines BM25 retrieval with risk-aware, embedding-augmented, and hybrid reranking, followed by constrained retrieval-augmented generation with explicit document citations. Experiments on a synthetic 900-document corpus and 10 queries produced the highest descriptive retrieval scores under risk-based reranking, with P@10 = 1.00 and NDCG@10 = 0.94, compared with 0.94 and 0.81 for BM25. The best hybrid and BM25+Semantic configurations reached NDCG@10 values of 0.94 and 0.88, respectively. All 41 evaluable generated bullets passed the lexical coverage threshold; an automated LLM judge classified 36 as supported, one as partially supported, and four as unsupported. These results demonstrate the feasibility of Guardian Crawler as a controlled testbed but do not establish statistical superiority, human-validated faithfulness, or transfer to live-web investigative environments.
J. Castillo, Santosh Nukavarapu, Ravi Mukkamala· 0 citations
We introduce SciRet, a compute-aware empirical study of retrieval-augmented generation for scientific question answering over CORD-19. Rather than proposing a new model, we evaluate a fixed scientific RAG pipeline across three corpus scales: 1,034 chunks (1K papers), 5,160 chunks (5K papers), and 15,480 chunks (15K papers). The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation. Across these settings, hybrid retrieval is more robust than either sparse-only or dense-only retrieval in our setting, reaching Recall@10 of 1.000 at 1K and 15K. In contrast, an MS MARCO-trained cross-encoder reranker reduces precision on the scientific corpus, suggesting that domain mismatch can outweigh the benefits of stronger query-passage interaction. Generation faithfulness measured with RAGAS increases with corpus scale in our setup. Retrieval evaluation uses pseudo-relevance labels derived from the hybrid system, so we treat the results as controlled comparative evidence rather than a benchmark claim. We release code, indexes, and evaluation outputs to support replication and follow-up studies.
Kaysarul Anas Apurba, Mahade Hasan, Rofiqul Alam Shehab et al.· 0 citations
