{"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/tourrank-utilizing-large-language-models-for","title":"TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy","arxiv_id":"2406.11678","date":"2024-06-17","proceeding":null,"authors":["Yiqun Chen","Qi Liu","Yi Zhang","Weiwei Sun","Daiting Shi","Jiaxin Mao","Dawei Yin"],"abstract":"Large Language Models (LLMs) are increasingly employed in zero-shot documents ranking, yielding commendable results. However, several significant challenges still persist in LLMs for ranking: (1) LLMs are constrained by limited input length, precluding them from processing a large number of documents simultaneously; (2) The output document sequence is influenced by the input order of documents, resulting in inconsistent ranking outcomes; (3) Achieving a balance between cost and ranking performance is quite challenging. To tackle these issues, we introduce a novel documents ranking method called TourRank, which is inspired by the tournament mechanism. This approach alleviates the impact of LLM's limited input length through intelligent grouping, while the tournament-like points system ensures robust ranking, mitigating the influence of the document input sequence. We test TourRank with different LLMs on the TREC DL datasets and the BEIR benchmark. Experimental results show that TourRank achieves state-of-the-art performance at a reasonable cost.","url_abs":"https://arxiv.org/abs/2406.11678v1","url_pdf":"https://arxiv.org/pdf/2406.11678v1.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":"tourrank-utilizing-large-language-models-for","repo_url":"https://github.com/chenyiqun/TourRank","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2406.11678","atlas_url":"https://app.syntology.ai/?focus=2406.11678","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11678"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/chenyiqun/TourRank","reach":{"status":"ok"}}],"summary":{"ran":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"77b37ebb30830242","entry":"get_post_role_prompt","repo":"chenyiqun/TourRank","repo_kind":"official","path":"TourRank_multiprocessing.py","file_url":"https://github.com/chenyiqun/TourRank/blob/HEAD/TourRank_multiprocessing.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"77b37ebb30830242"}},{"code_sha256_prefix":"be3029beb4726173","entry":"get_prefix_role_prompt","repo":"chenyiqun/TourRank","repo_kind":"official","path":"TourRank_multiprocessing.py","file_url":"https://github.com/chenyiqun/TourRank/blob/HEAD/TourRank_multiprocessing.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"be3029beb4726173"}},{"code_sha256_prefix":"74aeaeb3787e659c","entry":"get_response","repo":"chenyiqun/TourRank","repo_kind":"official","path":"TourRank_multiprocessing.py","file_url":"https://github.com/chenyiqun/TourRank/blob/HEAD/TourRank_multiprocessing.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"74aeaeb3787e659c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}