{"id":"https://openalex.org/W7108322660","doi":"https://doi.org/10.1145/3767695.3769493","title":"Do Large Language Models Favor Recent Content? A Study on Recency Bias in LLM-Based Reranking","display_name":"Do Large Language Models Favor Recent Content? A Study on Recency Bias in LLM-Based Reranking","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7108322660","doi":"https://doi.org/10.1145/3767695.3769493"},"language":null,"primary_location":{"id":"doi:10.1145/3767695.3769493","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3767695.3769493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hanpei Fang","orcid":"https://orcid.org/0009-0009-5117-5621"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hanpei Fang","raw_affiliation_strings":["Waseda University, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0009-5117-5621","affiliations":[{"raw_affiliation_string":"Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sijie Tao","orcid":"https://orcid.org/0000-0002-6751-5303"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sijie Tao","raw_affiliation_strings":["Waseda University, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0002-6751-5303","affiliations":[{"raw_affiliation_string":"Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nuo Chen","orcid":"https://orcid.org/0000-0001-8600-8203"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Nuo Chen","raw_affiliation_strings":["The Hong Kong Polytechnic University, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0001-8600-8203","affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kai-Xin Chang","orcid":"https://orcid.org/0009-0005-2675-8375"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kai-Xin Chang","raw_affiliation_strings":["Waseda University, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0005-2675-8375","affiliations":[{"raw_affiliation_string":"Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":null,"display_name":"Tetsuya Sakai","orcid":"https://orcid.org/0000-0002-6720-963X"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tetsuya Sakai","raw_affiliation_strings":["Waseda University, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0002-6720-963X","affiliations":[{"raw_affiliation_string":"Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150744194"],"apc_list":null,"apc_paid":null,"fwci":9.6909,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.98133822,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"85","last_page":"94"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.46860000491142273,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.46860000491142273,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.13660000264644623,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.09049999713897705,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.6912000179290771},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.6909000277519226},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5828999876976013},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3100999891757965}],"concepts":[{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.6912000179290771},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.6909000277519226},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5828999876976013},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.5376999974250793},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.4991999864578247},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41690000891685486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3562999963760376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3411000072956085},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3767695.3769493","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3767695.3769493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.43802371621131897,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W2039297031","https://openalex.org/W2053049304","https://openalex.org/W2108280221","https://openalex.org/W2148931201","https://openalex.org/W2155467656","https://openalex.org/W3160200580","https://openalex.org/W3184144760","https://openalex.org/W4252076394","https://openalex.org/W4368755400","https://openalex.org/W4384107234","https://openalex.org/W4385565351","https://openalex.org/W4385688511","https://openalex.org/W4385889719","https://openalex.org/W4387156646","https://openalex.org/W4387821331","https://openalex.org/W4389469970","https://openalex.org/W4389519118","https://openalex.org/W4389520758","https://openalex.org/W4389523765","https://openalex.org/W4391114255","https://openalex.org/W4391591659","https://openalex.org/W4394947904","https://openalex.org/W4396816204","https://openalex.org/W4399528455","https://openalex.org/W4400526908","https://openalex.org/W4400527956","https://openalex.org/W4401042345","https://openalex.org/W4401043313","https://openalex.org/W4402671660","https://openalex.org/W4404330874","https://openalex.org/W4404534210","https://openalex.org/W4404783366","https://openalex.org/W4405143965","https://openalex.org/W4405144074","https://openalex.org/W4405253355","https://openalex.org/W4405298817","https://openalex.org/W4405578323","https://openalex.org/W4405655184","https://openalex.org/W4409657425","https://openalex.org/W4412377123","https://openalex.org/W4412377194","https://openalex.org/W4412377345","https://openalex.org/W4412377930","https://openalex.org/W4412673545","https://openalex.org/W4412673546","https://openalex.org/W4415068613","https://openalex.org/W4416036398"],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,104],"(LLMs)":[3],"are":[4,72],"increasingly":[5],"deployed":[6],"in":[7,16,45,53,97,139,154],"information":[8,17],"systems,":[9],"including":[10],"being":[11],"used":[12],"as":[13,92,94],"second-stage":[14],"rerankers":[15],"retrieval":[18,51],"pipelines,":[19],"yet":[20],"their":[21],"susceptibility":[22],"to":[23,43,84,132],"recency":[24,152],"bias":[25,153],"has":[26],"received":[27],"little":[28],"attention.":[29],"We":[30,111],"investigate":[31],"whether":[32],"LLMs":[33,118,155],"implicitly":[34],"favour":[35],"newer":[36],"documents":[37],"by":[38,82,91,130],"prepending":[39],"artificial":[40],"publication":[41,79],"dates":[42],"passages":[44,71,121],"the":[46,76,106,115,158],"TREC":[47],"Deep":[48],"Learning":[49],"passage":[50],"collections":[52],"2021":[54],"(DL21)":[55],"and":[56,67,87,156],"2022":[57],"(DL22).":[58],"Across":[59],"seven":[60],"models,":[61],"GPT-3.5-turbo,":[62],"GPT-4o,":[63],"GPT-4,":[64],"LLaMA-3":[65],"8B/70B,":[66],"Qwen-2.5":[68],"7B/72B,":[69],"''fresh''":[70],"consistently":[73],"promoted,":[74],"shifting":[75],"Top-10's":[77],"mean":[78],"year":[80],"forward":[81],"up":[83,131],"4.78":[85],"years":[86],"moving":[88],"individual":[89],"items":[90],"many":[93],"95":[95],"ranks":[96],"our":[98,140],"listwise":[99],"reranking":[100],"experiments.":[101,143],"Although":[102],"larger":[103],"attenuate":[105],"effect,":[107],"none":[108],"eliminate":[109],"it.":[110],"also":[112],"observe":[113],"that":[114],"preference":[116,142],"of":[117,149,160],"between":[119],"two":[120],"with":[122],"an":[123],"identical":[124],"relevance":[125],"level":[126],"can":[127],"be":[128],"reversed":[129],"25%":[133],"on":[134],"average":[135],"after":[136],"date":[137],"injection":[138],"pairwise":[141],"These":[144],"findings":[145],"provide":[146],"quantitative":[147],"evidence":[148],"a":[150],"pervasive":[151],"highlight":[157],"importance":[159],"effective":[161],"bias-mitigation":[162],"strategies.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-12-03T00:00:00"}