{"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/domain-matched-pre-training-tasks-for-dense","title":"Domain-matched Pre-training Tasks for Dense Retrieval","arxiv_id":"2107.13602","date":"2021-07-28","proceeding":"Findings (NAACL) 2022 7","authors":["Barlas Oğuz","Kushal Lakhotia","Anchit Gupta","Patrick Lewis","Vladimir Karpukhin","Aleksandra Piktus","Xilun Chen","Sebastian Riedel","Wen-tau Yih","Sonal Gupta","Yashar Mehdad"],"abstract":"Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks. A notable exception is information retrieval, where additional pre-training has so far failed to produce convincing results. We show that, with the right pre-training setup, this barrier can be overcome. We demonstrate this by pre-training large bi-encoder models on 1) a recently released set of 65 million synthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations made available by pushshift.io. We evaluate on a set of information retrieval and dialogue retrieval benchmarks, showing substantial improvements over supervised baselines.","url_abs":"https://arxiv.org/abs/2107.13602v1","url_pdf":"https://arxiv.org/pdf/2107.13602v1.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":"domain-matched-pre-training-tasks-for-dense","repo_url":"https://github.com/facebookresearch/dpr-scale","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"passage-retrieval","task_name":"Passage Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/passage-retrieval-on-natural-questions","task":"Passage Retrieval","dataset":"Natural Questions","model":"DPR-PAQ","rank_in_archive_order":2,"of":10,"metrics":{"Precision@100":"89.22","Precision@20":"84.68"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2107.13602","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}