{"id":"https://openalex.org/W4415795809","doi":"https://doi.org/10.52202/079017-3671","title":"FLAME : Factuality-Aware Alignment for Large Language Models","display_name":"FLAME : Factuality-Aware Alignment for Large Language Models","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4415795809","doi":"https://doi.org/10.52202/079017-3671"},"language":"en","primary_location":{"id":"doi:10.52202/079017-3671","doi":"https://doi.org/10.52202/079017-3671","is_oa":false,"landing_page_url":"https://doi.org/10.52202/079017-3671","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 37","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2405.01525","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sheng-Chieh Lin","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng-Chieh Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060885692","display_name":"Luyu Gao","orcid":"https://orcid.org/0009-0006-5806-3022","observed_orcids":["https://orcid.org/0009-0006-5806-3022"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luyu Gao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071728146","display_name":"Barlas O\u011fuz","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barlas Oguz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110635444","display_name":"Wenhan Xiong","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenhan Xiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082997975","display_name":"Jimmy Lin","orcid":"https://orcid.org/0000-0002-0661-7189","observed_orcids":["https://orcid.org/0000-0002-0661-7189","https://orcid.org/0009-0007-1571-3854"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jimmy Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066873932","display_name":"Wen-tau Yih","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen-Tau Yih","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065674779","display_name":"Xilun Chen","orcid":"https://orcid.org/0009-0002-2876-3109","observed_orcids":["https://orcid.org/0009-0002-2876-3109","https://orcid.org/0000-0003-2184-6163"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xilun Chen","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":4.9253,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.96563837,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"115588","last_page":"115614"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8535000085830688,"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":0.8535000085830688,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.08829999715089798,"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":0.022700000554323196,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9729999899864197},{"id":"https://openalex.org/keywords/factual-consistency","display_name":"factual consistency","score":0.5339999794960022}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5444999933242798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38690000772476196},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3075999915599823},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30239999294281006},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2556999921798706},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.24560000002384186},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23389999568462372}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.52202/079017-3671","doi":"https://doi.org/10.52202/079017-3671","is_oa":false,"landing_page_url":"https://doi.org/10.52202/079017-3671","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 37","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2405.01525","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.01525","pdf_url":"https://arxiv.org/pdf/2405.01525","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2405.01525","doi":"https://doi.org/10.48550/arxiv.2405.01525","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.01525","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2405.01525","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.01525","pdf_url":"https://arxiv.org/pdf/2405.01525","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415795809.pdf","grobid_xml":"https://content.openalex.org/works/W4415795809.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4404781040","https://openalex.org/W4391871157","https://openalex.org/W7221758716","https://openalex.org/W7166862276","https://openalex.org/W4415335931","https://openalex.org/W7166874363","https://openalex.org/W4405470424","https://openalex.org/W7140141059","https://openalex.org/W4416034126","https://openalex.org/W4414938304"],"abstract_inverted_index":{"Alignment":[0],"is":[1],"a":[2,143],"standard":[3,126],"procedure":[4],"to":[5,12,33,43,58,72,118,137,183],"fine-tune":[6],"pre-trained":[7],"large":[8],"language":[9,15],"models":[10],"(LLMs)":[11],"follow":[13],"natural":[14],"instructions":[16],"and":[17,40,81,151,167],"serve":[18],"as":[19,107],"helpful":[20,140],"AI":[21],"assistants.":[22],"We":[23],"have":[24],"observed,":[25],"however,":[26],"that":[27,70,89,114,176],"the":[28,35,44,60,91,119,135],"conventional":[29],"alignment":[30,62,76,180],"process":[31,63],"fails":[32],"enhance":[34],"factual":[36,106,186],"accuracy":[37],"of":[38,46,146,164],"LLMs,":[39],"often":[41,148],"leads":[42],"generation":[45],"more":[47,64,139,152,185],"false":[48],"facts":[49],"(i.e.":[50],"hallucination).":[51],"In":[52,85],"this":[53],"paper,":[54],"we":[55,87,159],"study":[56],"how":[57],"make":[59],"LLM":[61,92,136],"factual,":[65],"by":[66],"first":[67],"identifying":[68],"factors":[69],"lead":[71],"hallucination":[73],"in":[74,125],"both":[75],"steps:\\":[77],"supervised":[78],"fine-tuning":[79],"(SFT)":[80],"reinforcement":[82],"learning":[83],"(RL).":[84],"particular,":[86],"find":[88],"training":[90],"on":[93,110,142,156],"new":[94],"knowledge":[95],"or":[96],"unfamiliar":[97],"texts":[98],"can":[99,128],"encourage":[100,130],"hallucination.":[101],"This":[102],"makes":[103],"SFT":[104,166],"less":[105],"it":[108,133],"trains":[109],"human":[111],"labeled":[112],"data":[113],"may":[115],"be":[116],"novel":[117],"LLM.":[120],"Furthermore,":[121],"reward":[122],"functions":[123],"used":[124],"RL":[127,169],"also":[129],"hallucination,":[131],"because":[132],"guides":[134,181],"provide":[138],"responses":[141,187],"diverse":[144],"set":[145],"instructions,":[147],"preferring":[149],"longer":[150],"detailed":[153],"responses.":[154],"Based":[155],"these":[157],"observations,":[158],"propose":[160],"factuality-aware":[161,165,168,179],"alignment,":[162],"comprised":[163],"through":[170],"direct":[171],"preference":[172],"optimization.":[173],"Experiments":[174],"show":[175],"our":[177],"proposed":[178],"LLMs":[182],"output":[184],"while":[188],"maintaining":[189],"instruction-following":[190],"capability.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":7}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2025-11-03T00:00:00"}
