{"id":"https://openalex.org/W4416036053","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.1038","title":"DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning","display_name":"DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416036053","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.1038"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.emnlp-main.1038","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1038","pdf_url":"https://aclanthology.org/2025.emnlp-main.1038.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 2025 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/2025.emnlp-main.1038.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067393636","display_name":"Tanmay Parekh","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tanmay Parekh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088166204","display_name":"Kartik Mehta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kartik Mehta","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056269049","display_name":"Ninareh Mehrabi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ninareh Mehrabi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087096372","display_name":"Kai-Wei Chang","orcid":"https://orcid.org/0000-0001-5365-0072"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kai-Wei Chang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5030248499","display_name":"Nanyun Peng","orcid":"https://orcid.org/0000-0002-8509-6595"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nanyun Peng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5067393636"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2134,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8487992,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"20571","last_page":"20593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.08789999783039093,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.08789999783039093,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.05490000173449516,"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/T10028","display_name":"Topic Modeling","score":0.05270000174641609,"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/event","display_name":"Event (particle physics)","score":0.5151000022888184},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.27649998664855957},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.27649998664855957},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2517000138759613},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2401999980211258}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5986999869346619},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5177000164985657},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5151000022888184},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.27649998664855957},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2662999927997589},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2401999980211258},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.23589999973773956},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2345999926328659}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.emnlp-main.1038","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1038","pdf_url":"https://aclanthology.org/2025.emnlp-main.1038.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 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.emnlp-main.1038","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1038","pdf_url":"https://aclanthology.org/2025.emnlp-main.1038.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 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6710492401","display_name":"CAREER: Insertion-Based Natural Language Generation","funder_award_id":"2339766","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6755165505","display_name":null,"funder_award_id":"award","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6894402473","display_name":null,"funder_award_id":"Fellowship","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416036053.pdf","grobid_xml":"https://content.openalex.org/works/W4416036053.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Zero-shot":[0],"Event":[1],"Detection":[2],"(ED),":[3],"the":[4,26,34,43,64,91,95,107,143],"task":[5,65],"of":[6,45,66],"identifying":[7],"event":[8,28,77,83],"mentions":[9],"in":[10,23],"natural":[11],"language":[12],"text":[13],"without":[14],"any":[15],"training":[16],"data,":[17],"is":[18],"critical":[19],"for":[20,50],"document":[21],"understanding":[22],"specialized":[24],"domains.Understanding":[25],"complex":[27],"ontology,":[29],"extracting":[30],"domain-specific":[31],"triggers":[32],"from":[33],"passage,":[35],"and":[36,41,70,122,134],"structuring":[37],"them":[38],"appropriately":[39],"overloads":[40],"limits":[42],"utility":[44],"Large":[46],"Language":[47],"Models":[48],"(LLMs)":[49],"zero-shot":[51,151],"ED.To":[52],"this":[53],"end,":[54],"we":[55,125],"propose":[56],"DICORE,":[57],"a":[58,149],"divergent-convergent":[59],"reasoning":[60,74,88,135],"framework":[61],"that":[62],"decouples":[63],"ED":[67,152],"using":[68,98],"Dreamer":[69],"Grounder.Dreamer":[71],"encourages":[72],"divergent":[73],"through":[75],"openended":[76],"discovery,":[78],"which":[79],"helps":[80],"to":[81,89,110],"boost":[82],"coverage.Conversely,":[84],"Grounder":[85],"introduces":[86],"convergent":[87],"align":[90],"freeform":[92],"predictions":[93],"with":[94],"task-specific":[96],"instructions":[97],"finite-state":[99],"machine":[100],"guided":[101],"constrained":[102],"decoding.Additionally,":[103],"an":[104],"LLM-Judge":[105],"verifies":[106],"final":[108],"outputs":[109],"ensure":[111],"high":[112],"precision.Through":[113],"extensive":[114],"experiments":[115],"on":[116],"six":[117],"datasets":[118],"across":[119],"five":[120],"domains":[121],"nine":[123],"LLMs,":[124],"demonstrate":[126],"how":[127],"DICORE":[128,147],"consistently":[129],"outperforms":[130],"prior":[131],"zero-shot,":[132],"transfer-learning,":[133],"baselines,":[136],"achieving":[137],"4-7%":[138],"average":[139],"F1":[140],"gains":[141],"over":[142],"best":[144],"baseline":[145],"-establishing":[146],"as":[148],"strong":[150],"framework.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-11-08T00:00:00"}
