{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quam-adaptive-retrieval-through-query","title":"Quam: Adaptive Retrieval through Query Affinity Modelling","arxiv_id":"2410.20286","date":"2024-10-26","proceeding":null,"authors":["Mandeep Rathee","Sean MacAvaney","Avishek Anand"],"abstract":"Building relevance models to rank documents based on user information needs is a central task in information retrieval and the NLP community. Beyond the direct ad-hoc search setting, many knowledge-intense tasks are powered by a first-stage retrieval stage for context selection, followed by a more involved task-specific model. However, most first-stage ranking stages are inherently limited by the recall of the initial ranking documents. Recently, adaptive re-ranking techniques have been proposed to overcome this issue by continually selecting documents from the whole corpus, rather than only considering an initial pool of documents. However, so far these approaches have been limited to heuristic design choices, particularly in terms of the criteria for document selection. In this work, we propose a unifying view of the nascent area of adaptive retrieval by proposing, Quam, a \\textit{query-affinity model} that exploits the relevance-aware document similarity graph to improve recall, especially for low re-ranking budgets. Our extensive experimental evidence shows that our proposed approach, Quam improves the recall performance by up to 26\\% over the standard re-ranking baselines. Further, the query affinity modelling and relevance-aware document graph modules can be injected into any adaptive retrieval approach. The experimental results show the existing adaptive retrieval approach improves recall by up to 12\\%. The code of our work is available at \\url{https://github.com/Mandeep-Rathee/quam}.","url_abs":"https://arxiv.org/abs/2410.20286v1","url_pdf":"https://arxiv.org/pdf/2410.20286v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"quam-adaptive-retrieval-through-query","repo_url":"https://github.com/Mandeep-Rathee/quam","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"re-ranking","task_name":"Re-Ranking"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.20286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.20286"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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