{"id":"https://openalex.org/W4412944792","doi":"https://doi.org/10.18653/v1/2025.acl-long.1089","title":"Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering","display_name":"Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412944792","doi":"https://doi.org/10.18653/v1/2025.acl-long.1089"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.acl-long.1089","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1089","pdf_url":"https://aclanthology.org/2025.acl-long.1089.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.acl-long.1089.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064216598","display_name":"Rong Zhu","orcid":"https://orcid.org/0000-0003-0218-9241"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rongzhi Zhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021232406","display_name":"Xiangyu Liu","orcid":"https://orcid.org/0000-0001-8864-3411"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangyu Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101848528","display_name":"Zequn Sun","orcid":"https://orcid.org/0000-0003-4177-9199"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zequn Sun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100397317","display_name":"Yiwei Wang","orcid":"https://orcid.org/0000-0002-5516-2897"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiwei Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100604966","display_name":"Wei Hu","orcid":"https://orcid.org/0000-0003-3068-6333"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Hu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.0381,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.9832245,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"22362","last_page":"22375"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9833999872207642,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9833999872207642,"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"}},{"id":"https://openalex.org/T13274","display_name":"Expert finding and Q&A systems","score":0.9521999955177307,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9309999942779541,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/question-answering","display_name":"Question answering","score":0.8412267565727234},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7433778047561646},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.6795432567596436},{"id":"https://openalex.org/keywords/hop","display_name":"Hop (telecommunications)","score":0.6075733304023743},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1052941083908081}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8412267565727234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7433778047561646},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6795432567596436},{"id":"https://openalex.org/C25906391","wikidata":"https://www.wikidata.org/wiki/Q1432381","display_name":"Hop (telecommunications)","level":2,"score":0.6075733304023743},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1052941083908081}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.acl-long.1089","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1089","pdf_url":"https://aclanthology.org/2025.acl-long.1089.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.acl-long.1089","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1089","pdf_url":"https://aclanthology.org/2025.acl-long.1089.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G3137102087","display_name":null,"funder_award_id":"62272219","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412944792.pdf","grobid_xml":"https://content.openalex.org/works/W4412944792.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2384605597","https://openalex.org/W2387743295","https://openalex.org/W2117210722","https://openalex.org/W2589759689","https://openalex.org/W3082787378","https://openalex.org/W2136007095","https://openalex.org/W2366230879"],"abstract_inverted_index":{"In":[0],"this":[1,41],"paper,":[2],"we":[3,43],"identify":[4],"a":[5,45,68,87,107],"critical":[6],"problem,":[7,42],"\"lost-in-retrieval\",":[8],"in":[9,20,75,139],"retrieval-augmented":[10],"multihop":[11],"question":[12],"answering":[13],"(QA):":[14],"the":[15,26,31,37,83,111],"key":[16,61],"entities":[17,62],"are":[18,103],"missed":[19],"LLMs'":[21],"sub-question":[22,57,101],"decomposition.\"Lost-in-retrieval\"":[23],"significantly":[24],"degrades":[25],"retrieval":[27,47,77,94],"performance,":[28],"which":[29,53],"disrupts":[30],"reasoning":[32],"chain":[33,89],"and":[34,48,63,78,95,100,122,130,142],"leads":[35,91],"to":[36,92,105,110],"incorrect":[38],"answers.To":[39],"resolve":[40],"propose":[44],"progressive":[46],"rewriting":[49,79],"method,":[50],"namely":[51],"ChainRAG,":[52],"sequentially":[54],"handles":[55],"each":[56],"by":[58],"completing":[59],"missing":[60],"retrieving":[64],"relevant":[65],"sentences":[66,99],"from":[67],"sentence":[69],"graph":[70],"for":[71],"answer":[72,109],"generation.Each":[73],"step":[74],"our":[76],"process":[80],"builds":[81],"upon":[82],"previous":[84],"one,":[85],"creating":[86],"seamless":[88],"that":[90,134],"accurate":[93],"answers.Finally,":[96],"all":[97],"retrieved":[98],"answers":[102],"integrated":[104],"generate":[106],"comprehensive":[108],"original":[112],"question.We":[113],"evaluate":[114],"ChainRAG":[115,135],"on":[116],"three":[117,124],"multi-hop":[118],"QA":[119],"datasets-MuSiQue,":[120],"2Wiki,":[121],"HotpotQA-using":[123],"large":[125],"language":[126],"models:":[127],"GPT4o-mini,":[128],"Qwen2.5-72B,":[129],"GLM-4-Plus.Empirical":[131],"results":[132],"demonstrate":[133],"consistently":[136],"outperforms":[137],"baselines":[138],"both":[140],"effectiveness":[141],"efficiency.":[143]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
