{"id":"https://openalex.org/W4404781754","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.1259","title":"LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering","display_name":"LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4404781754","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.1259"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2024.emnlp-main.1259","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.emnlp-main.1259","pdf_url":"https://aclanthology.org/2024.emnlp-main.1259.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 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2024.emnlp-main.1259.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047419128","display_name":"Qi Zhao","orcid":"https://orcid.org/0000-0003-3054-8934"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Qingfei Zhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100778734","display_name":"Ruobing Wang","orcid":"https://orcid.org/0000-0001-8363-0371"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruobing Wang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035985651","display_name":"Yukuo Cen","orcid":"https://orcid.org/0000-0001-5682-2810"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yukuo Cen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051666209","display_name":"Daren Zha","orcid":"https://orcid.org/0009-0002-6042-3454"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daren Zha","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006843261","display_name":"Shicheng Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shicheng Tan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052284218","display_name":"Yuxiao Dong","orcid":"https://orcid.org/0000-0002-6092-2002"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuxiao Dong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Jie Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Tang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5047419128"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0138,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.9461116,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"22600","last_page":"22632"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9685999751091003,"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":0.9685999751091003,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9449999928474426,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9387999773025513,"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/perspective","display_name":"Perspective (graphical)","score":0.7754442691802979},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.7339513897895813},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.69413161277771},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6563496589660645},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6269928812980652},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4495604336261749},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3050594925880432},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.12394833564758301},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.09821876883506775},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.052397310733795166}],"concepts":[{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.7754442691802979},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7339513897895813},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.69413161277771},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6563496589660645},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6269928812980652},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4495604336261749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3050594925880432},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.12394833564758301},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.09821876883506775},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.052397310733795166},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2024.emnlp-main.1259","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.emnlp-main.1259","pdf_url":"https://aclanthology.org/2024.emnlp-main.1259.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 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2024.emnlp-main.1259","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.emnlp-main.1259","pdf_url":"https://aclanthology.org/2024.emnlp-main.1259.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 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6048029205","display_name":null,"funder_award_id":"62276148","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/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404781754.pdf","grobid_xml":"https://content.openalex.org/works/W4404781754.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2384605597","https://openalex.org/W2387743295","https://openalex.org/W3082787378","https://openalex.org/W2136007095","https://openalex.org/W2366230879","https://openalex.org/W3208425359","https://openalex.org/W2349927912","https://openalex.org/W3159777597","https://openalex.org/W4212839359"],"abstract_inverted_index":{"Long-Context":[0],"Question":[1],"Answering":[2],"(LCQA),":[3],"a":[4,76,104],"challenging":[5],"task,":[6],"aims":[7],"to":[8,13,17,68,87,109],"reason":[9],"over":[10],"longcontext":[11],"documents":[12],"yield":[14],"accurate":[15],"answers":[16],"questions.Existing":[18],"long-context":[19,51,93,124],"Large":[20],"Language":[21],"Models":[22],"(LLMs)":[23],"for":[24,85],"LCQA":[25,86],"often":[26],"struggle":[27],"with":[28],"the":[29,32,49,149,152],"\"lost":[30],"in":[31,57],"middle\"":[33],"issue.Retrieval-Augmented":[34],"Generation":[35],"(RAG)":[36],"mitigates":[37],"this":[38,71],"issue":[39],"by":[40,127,132,138],"providing":[41],"external":[42],"factual":[43,65,99],"evidence.However,":[44],"its":[45,54],"chunking":[46],"strategy":[47],"disrupts":[48],"global":[50,96],"information,":[52],"and":[53,79,98,112,134,145,155,158],"low-quality":[55],"retrieval":[56],"long":[58],"contexts":[59],"hinders":[60],"LLMs":[61,125],"from":[62],"identifying":[63],"effective":[64],"details":[66],"due":[67],"substantial":[69],"noise.To":[70],"end,":[72],"we":[73,140],"propose":[74],"LongRAG,":[75],"general,":[77],"dual-perspective,":[78],"robust":[80],"LLM-based":[81],"RAG":[82,130,136],"system":[83],"paradigm":[84],"enhance":[88],"RAG's":[89],"understanding":[90],"of":[91,151],"complex":[92],"knowledge":[94],"(i.e.,":[95],"information":[97],"details).We":[100],"design":[101],"LongRAG":[102,121],"as":[103],"plug-and-play":[105],"paradigm,":[106],"facilitating":[107],"adaptation":[108],"various":[110],"domains":[111],"LLMs.Extensive":[113],"experiments":[114],"on":[115],"three":[116],"multihop":[117],"datasets":[118],"demonstrate":[119],"that":[120],"significantly":[122],"outperforms":[123],"(up":[126,131,137],"6.94%),":[128],"advanced":[129],"6.16%),":[133],"Vanilla":[135],"17.25%).Furthermore,":[139],"conduct":[141],"quantitative":[142],"ablation":[143],"studies":[144],"multidimensional":[146],"analyses,":[147],"highlighting":[148],"effectiveness":[150],"system's":[153],"components":[154],"finetuning":[156],"strategies.Data":[157],"code":[159],"are":[160],"available":[161],"at":[162],"https://github.com/QingFei1/LongRAG.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}