{"id":"https://openalex.org/W7126450383","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.4","title":"FlexiQA: Leveraging LLM\u2019s Evaluation Capabilities for Flexible Knowledge Selection in Open-domain Question Answering","display_name":"FlexiQA: Leveraging LLM\u2019s Evaluation Capabilities for Flexible Knowledge Selection in Open-domain Question Answering","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W7126450383","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.4"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2024.findings-eacl.4","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.4","pdf_url":"https://aclanthology.org/2024.findings-eacl.4.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":"Findings of the Association for Computational Linguistics: EACL 2024","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2024.findings-eacl.4.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124550451","display_name":"Yuhan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuhan Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005856697","display_name":"Shuqi Li","orcid":"https://orcid.org/0009-0007-4467-6773"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuqi Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124520806","display_name":"Rui Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rui Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3055,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.70271366,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"56","last_page":"66"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8012999892234802,"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":0.8012999892234802,"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":0.03060000017285347,"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/T12031","display_name":"Speech and dialogue systems","score":0.02449999935925007,"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/question-answering","display_name":"Question answering","score":0.6614999771118164},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.53329998254776},{"id":"https://openalex.org/keywords/knowledge-based-systems","display_name":"Knowledge-based systems","score":0.33660000562667847},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.3287999927997589},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.3091999888420105},{"id":"https://openalex.org/keywords/information-system","display_name":"Information system","score":0.28679999709129333}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6614999771118164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6608999967575073},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.53329998254776},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3319000005722046},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.32899999618530273},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3156000077724457},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C180198813","wikidata":"https://www.wikidata.org/wiki/Q121182","display_name":"Information system","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2024.findings-eacl.4","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.4","pdf_url":"https://aclanthology.org/2024.findings-eacl.4.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":"Findings of the Association for Computational Linguistics: EACL 2024","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2024.findings-eacl.4","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.4","pdf_url":"https://aclanthology.org/2024.findings-eacl.4.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":"Findings of the Association for Computational Linguistics: EACL 2024","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G53857530","display_name":null,"funder_award_id":"62122089","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320318398","display_name":"Ant Group","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322499","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7126450383.pdf","grobid_xml":"https://content.openalex.org/works/W7126450383.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Nowadays,":[0],"large":[1],"language":[2],"models":[3],"(LLMs)":[4],"have":[5],"demonstrated":[6],"their":[7,75],"ability":[8],"to":[9,33,61,66,95,111,124,139,158],"be":[10],"a":[11,49,80,102,109,141,156],"powerful":[12],"knowledge":[13,41,60,97],"generator":[14],"of":[15,38,58,70,93,178],"generate-thenread":[16],"paradigm":[17,25,123],"for":[18],"open-domain":[19],"question":[20],"answering":[21],"(ODQA).However":[22],"this":[23],"new":[24,81],"mainly":[26],"suffers":[27],"from":[28,144],"the":[29,44,56,62,68,89,121,126,128,134,151,160,171,176],"\"hallucination\"":[30],"and":[31,73,99,148],"struggles":[32],"handle":[34],"time-sensitive":[35,117],"issue":[36],"because":[37],"its":[39],"expensive":[40],"update":[42],"costs.On":[43],"other":[45],"hand,":[46],"retrieve-then-read,":[47],"as":[48,108,136],"traditional":[50],"paradigm,":[51],"is":[52,115],"more":[53],"limited":[54],"by":[55],"relevance":[57],"acquired":[59],"given":[63,101],"question.In":[64],"order":[65],"combine":[67],"strengths":[69],"both":[71],"paradigms,":[72],"overcome":[74],"respective":[76],"shortcomings,":[77],"we":[78,87,104,119,131,154],"design":[79],"pipeline":[82],"called":[83],"\"Flex-iQA\",":[84],"in":[85],"which":[86],"utilize":[88],"diverse":[90],"evaluation":[91],"capabilities":[92],"LLMs":[94],"select":[96,140],"effectively":[98],"flexibly.First,":[100],"question,":[103],"prompt":[105,133],"an":[106,137],"LLM":[107,135],"discriminator":[110],"identify":[112],"whether":[113],"it":[114],"time-sensitive.For":[116],"questions,":[118,130],"follow":[120],"retrievethen-read":[122],"obtain":[125],"answer.For":[127],"non-time-sensitive":[129],"further":[132],"evaluator":[138],"better":[142],"document":[143],"two":[145],"perspectives:":[146],"factuality":[147],"relevance.Based":[149],"on":[150,166],"selected":[152],"document,":[153],"leverage":[155],"reader":[157],"get":[159],"final":[161],"answer.We":[162],"conduct":[163],"extensive":[164],"experiments":[165],"three":[167],"widely-used":[168],"ODQA":[169],"benchmarks,":[170],"experimental":[172],"results":[173],"fully":[174],"confirm":[175],"effectiveness":[177],"our":[179],"approach.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-02T00:00:00"}
