Jul 2026· Annual International ACM SIGIR Conference on Research and Development in Information Retrieval· 0 citations· 33 references
Computer Science
TL;DR
This work conducts an empirical study of how irrelevant retrieved passages affect downstream generation, and proposes a lightweight, context-size classification module that dynamically predicts how much context is required based on query-specific needs.
Abstract
Retrieval augmented generation (RAG) combines language models with external corpora to support knowledge-intensive tasks, such as open-domain question answering. Standard RAG systems typically employ a fixed top-k retrieval strategy, retrieving the same number of passages regardless of query needs. This can lead to either insufficient evidence, or the inclusion of irrelevant contexts that lead to a degradation of generation performance. In this work, we conduct an empirical study of how irrelevant retrieved passages affect downstream generation, analyzing their impact across multiple standard generator models. Building on these insights, we propose a lightweight, context-size classification module that dynamically predicts how much context is required based on query-specific needs. We integrate this approach into a full RAG pipeline and demonstrate improved performance over several baselines.
Large language models may suffer from insufficient context use and unsupported generation in question answering tasks that require external knowledge. This study compares the main strategies affecting retrieval and generation performance in retrieval-augmented generation systems within a common experimental setting. In...
Buğra Şimşek, Korhan Sevinç, Helen Parlar et al.· Signal Processing and Commun...· 0 citations
A novel method for evaluating query reformulation in Retrieval-Augmented Generation (RAG) without relying on costly manual annotations of relevant documents is proposed, which measures both semantic similarity and downstream retrieval performance.
S. Markowski, Konrad Wojtasik, Adrian Berdowski et al.· International Conference on...· 0 citations
Experimental results show that SAC-RAG reduces token consumption by 38%–58% at the cost of only a 1–2 percentage point EM drop, with EM actually improving after compression for reasoning-type questions, achieving the optimal quality–efficiency trade-off in terms of token consumption.
Deyu Zhang, Hongqiang Yu, Jinze Huo et al.· IEEE Access· 0 citations
Chunk Coverage (CC), an oracle-independent test adequacy criterion for testing the retrieval component of RAG systems, is introduced and results show that CC captures retrieval diversity relevant to effective testing without requiring test oracles.
Jinhan Kim, Samuele Pasini, Paolo Tonella· arXiv.org· 1 citation
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,...
The idea of context is no longer considered secondary in the construction of language-model systems. With the use of local Retrieval-Augmented Generation, even a tiny modification of the prompt or the context might produce another set of retrievals, citations, and ultimately different answers; however, in practice, tes...
Rahul Reddy Gangapuram, William B. Andreopoulos· International Conference on...· 0 citations
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