{"id":"https://openalex.org/W4399911186","doi":"https://doi.org/10.1145/3637528.3672065","title":"FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering","display_name":"FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4399911186","doi":"https://doi.org/10.1145/3637528.3672065"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3672065","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3672065","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672065","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672065","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029097679","display_name":"Tianchi Cai","orcid":"https://orcid.org/0000-0003-1503-6519"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tianchi Cai","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhiwen Tan","orcid":"https://orcid.org/0009-0004-4833-1375"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiwen Tan","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081007833","display_name":"Xierui Song","orcid":"https://orcid.org/0000-0002-4580-1683"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xierui Song","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012283901","display_name":"Tao Sun","orcid":"https://orcid.org/0000-0001-6357-6726"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao Sun","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072368959","display_name":"Jiyan Jiang","orcid":"https://orcid.org/0000-0002-1083-2834"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyan Jiang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100582105","display_name":"Yunqi Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yunqi Xu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008134938","display_name":"Yinger Zhang","orcid":"https://orcid.org/0000-0001-6453-7462"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yinger Zhang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053242349","display_name":"Jinjie Gu","orcid":"https://orcid.org/0000-0001-7596-4945"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinjie Gu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5029097679"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3632,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.93060275,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"199","last_page":"210"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9941999912261963,"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":0.9922000169754028,"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/computer-science","display_name":"Computer science","score":0.865572452545166},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.6457501649856567},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5764686465263367},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5713352560997009},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.543910562992096},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5387673377990723},{"id":"https://openalex.org/keywords/helpfulness","display_name":"Helpfulness","score":0.5178139805793762},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4409751296043396},{"id":"https://openalex.org/keywords/wordnet","display_name":"WordNet","score":0.41301488876342773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39634743332862854},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.33877938985824585},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.21994328498840332}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.865572452545166},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6457501649856567},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5764686465263367},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5713352560997009},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.543910562992096},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5387673377990723},{"id":"https://openalex.org/C2781265381","wikidata":"https://www.wikidata.org/wiki/Q5710255","display_name":"Helpfulness","level":2,"score":0.5178139805793762},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4409751296043396},{"id":"https://openalex.org/C157659113","wikidata":"https://www.wikidata.org/wiki/Q533822","display_name":"WordNet","level":2,"score":0.41301488876342773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39634743332862854},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33877938985824585},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.21994328498840332},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3637528.3672065","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3672065","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672065","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2406.13779","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2406.13779","pdf_url":"https://arxiv.org/pdf/2406.13779","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3637528.3672065","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3672065","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672065","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4399911186.pdf"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2912924812","https://openalex.org/W2950681488","https://openalex.org/W2964223283","https://openalex.org/W3175060421","https://openalex.org/W4226278401","https://openalex.org/W4285294723","https://openalex.org/W4287116904","https://openalex.org/W4385568240","https://openalex.org/W4385572464","https://openalex.org/W4388778348","https://openalex.org/W4389519254","https://openalex.org/W4400267554","https://openalex.org/W4401863569"],"related_works":["https://openalex.org/W2613921548","https://openalex.org/W4285360723","https://openalex.org/W4281847990","https://openalex.org/W2488228222","https://openalex.org/W3037056935","https://openalex.org/W2002563848","https://openalex.org/W2934621214","https://openalex.org/W1498449133","https://openalex.org/W2611407113","https://openalex.org/W2092282862"],"abstract_inverted_index":{"Retrieval":[0],"Augmented":[1],"Generation":[2],"(RAG)":[3],"has":[4],"become":[5],"prevalent":[6],"in":[7,56,75,90,124,172],"question-answering":[8,25],"(QA)":[9],"tasks":[10],"due":[11],"to":[12,19,86,164],"its":[13],"ability":[14],"of":[15,23,30,51,93,127,145,174,187,194,196],"utilizing":[16],"search":[17],"engine":[18],"enhance":[20],"the":[21,28,49,57,91,143,166,185],"quality":[22],"long-form":[24,59],"(LFQA).":[26],"Despite":[27],"emergence":[29],"various":[31],"open":[32],"source":[33],"methods":[34,136],"and":[35,53,96,121,155,182,200],"web-enhanced":[36,76],"commercial":[37],"systems":[38],"such":[39],"as":[40,137],"Bing":[41],"Chat,":[42],"two":[43,98],"critical":[44],"problems":[45],"remain":[46],"unsolved,":[47],"i.e.,":[48],"lack":[50],"factuality":[52,105],"clear":[54,88],"logic":[55,89],"generated":[58],"answers.":[60],"In":[61,158],"this":[62],"paper,":[63],"we":[64,79,102],"remedy":[65],"these":[66],"issues":[67],"via":[68],"a":[69,82,104,110],"systematic":[70],"study":[71],"on":[72,109,152],"answer":[73],"generation":[74,92],"LFQA.":[77],"Specifically,":[78],"first":[80],"propose":[81,103],"novel":[83],"outline-enhanced":[84],"generator":[85],"achieve":[87],"multifaceted":[94],"answers":[95],"construct":[97],"datasets":[99,199],"accordingly.":[100],"Then":[101],"optimization":[106],"method":[107,151,163],"based":[108],"carefully":[111],"designed":[112],"doubly":[113],"fine-grained":[114,134],"RLHF":[115,135],"framework,":[116],"which":[117],"contains":[118],"automatic":[119],"evaluation":[120],"reward":[122],"modeling":[123],"different":[125],"levels":[126],"granularity.":[128],"Our":[129,198],"generic":[130],"framework":[131],"comprises":[132],"conventional":[133],"special":[138],"cases.":[139],"Extensive":[140],"experiments":[141],"verify":[142],"superiority":[144],"our":[146,162],"proposed":[147],"Factuality-optimized":[148],"RAG":[149],"(FoRAG)":[150],"both":[153],"English":[154],"Chinese":[156],"benchmarks.":[157],"particular,":[159],"when":[160],"applying":[161],"Llama2-7B-chat,":[165],"derived":[167],"model":[168],"FoRAG-L-7B":[169],"outperforms":[170],"WebGPT-175B":[171],"terms":[173],"three":[175],"commonly":[176],"used":[177],"metrics":[178],"(i.e.,":[179],"coherence,":[180],"helpfulness,":[181],"factuality),":[183],"while":[184],"number":[186],"parameters":[188],"is":[189],"much":[190],"smaller":[191],"(only":[192],"1/24":[193],"that":[195],"WebGPT-175B).":[197],"models":[201],"are":[202],"made":[203],"publicly":[204],"available":[205],"for":[206],"better":[207],"reproducibility.https://huggingface.co/forag":[208],"\u0142abelfootnote_dataset_url":[209]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
