{"id":"https://openalex.org/W4412888317","doi":"https://doi.org/10.18653/v1/2025.findings-acl.592","title":"Learning to Select In-Context Demonstration Preferred by Large Language Model","display_name":"Learning to Select In-Context Demonstration Preferred by Large Language Model","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412888317","doi":"https://doi.org/10.18653/v1/2025.findings-acl.592"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.findings-acl.592","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.592","pdf_url":"https://aclanthology.org/2025.findings-acl.592.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: ACL 2025","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-acl.592.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102601029","display_name":"Zheng Zhang","orcid":"https://orcid.org/0009-0008-9808-6020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101250501","display_name":"Shaocheng Lan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shaocheng Lan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039286973","display_name":"Lei Song","orcid":"https://orcid.org/0000-0002-9190-1482"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei Song","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066890376","display_name":"Jiang Bian","orcid":"https://orcid.org/0000-0001-9610-0847"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang Bian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103279917","display_name":"Yexin Li","orcid":"https://orcid.org/0000-0001-5907-8978"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yexin Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059778636","display_name":"Kan Ren","orcid":"https://orcid.org/0000-0003-3391-5795"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kan Ren","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":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"11345","last_page":"11360"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5873000025749207,"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":0.5873000025749207,"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/computer-science","display_name":"Computer science","score":0.7727829217910767},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.5655766725540161},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5626320838928223},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5059289336204529},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.47813737392425537},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41147664189338684},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.345309853553772}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7727829217910767},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.5655766725540161},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5626320838928223},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5059289336204529},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.47813737392425537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41147664189338684},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.345309853553772},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-acl.592","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.592","pdf_url":"https://aclanthology.org/2025.findings-acl.592.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: ACL 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-acl.592","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.592","pdf_url":"https://aclanthology.org/2025.findings-acl.592.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: ACL 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4300000071525574,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"},{"id":"https://openalex.org/F4320327675","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412888317.pdf","grobid_xml":"https://content.openalex.org/works/W4412888317.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2349222429","https://openalex.org/W2116230991","https://openalex.org/W3117430770","https://openalex.org/W2046058552","https://openalex.org/W2590751808","https://openalex.org/W1972377868","https://openalex.org/W2132709506","https://openalex.org/W2098730537","https://openalex.org/W2186895195","https://openalex.org/W2748557612"],"abstract_inverted_index":{"In-context":[0],"learning":[1,88],"(ICL)":[2],"enables":[3],"large":[4],"language":[5],"models":[6],"(LLMs)":[7],"to":[8,10,52,59,94,124],"adapt":[9],"new":[11],"tasks":[12],"during":[13],"inference":[14],"using":[15],"only":[16],"a":[17,84],"few":[18],"demonstrations.However,":[19],"ICL":[20,55,126],"performance":[21,113],"is":[22,68],"highly":[23],"dependent":[24],"on":[25,44,101],"the":[26,71,119],"selection":[27,98],"of":[28],"these":[29,40,79],"demonstrations.Recent":[30],"work":[31],"explores":[32],"retrieval-based":[33],"methods":[34,116],"for":[35,99],"selecting":[36,118],"query-specific":[37],"demonstrations,":[38,122],"but":[39],"approaches":[41],"often":[42],"rely":[43],"surrogate":[45],"objectives":[46],"such":[47],"as":[48],"metric":[49],"learning,":[50],"failing":[51],"directly":[53,95],"optimize":[54,96],"performance.Consequently,":[56],"they":[57],"struggle":[58],"identify":[60],"truly":[61],"beneficial":[62],"demonstrations.Moreover,":[63],"their":[64],"discriminative":[65],"retrieval":[66],"paradigm":[67],"ineffective":[69],"when":[70],"candidate":[72],"pool":[73],"lacks":[74],"sufficient":[75],"highquality":[76],"demonstrations.To":[77],"address":[78],"challenges,":[80],"we":[81],"propose":[82],"GENICL,":[83],"novel":[85],"generative":[86],"preference":[87],"framework":[89],"that":[90,109],"leverages":[91],"LLM":[92],"feedback":[93],"demonstration":[97],"ICL.Experiments":[100],"19":[102],"datasets":[103],"across":[104],"11":[105],"task":[106],"categories":[107],"demonstrate":[108],"GENICL":[110],"achieves":[111],"superior":[112],"than":[114],"existing":[115],"in":[117],"most":[120],"effective":[121],"leading":[123],"better":[125],"performance.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
