{"id":"https://openalex.org/W4416034587","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.413","title":"Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales","display_name":"Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034587","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.413"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.413","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.413","pdf_url":"https://aclanthology.org/2025.findings-emnlp.413.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: EMNLP 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-emnlp.413.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100583865","display_name":"Jianzhi Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"JianZhi Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100606922","display_name":"Xiaoyang Li","orcid":"https://orcid.org/0000-0002-4046-2934"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004433495","display_name":"Youcheng Pan","orcid":"https://orcid.org/0000-0002-8270-5455"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Youcheng Pan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Shiwei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiwei Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051936733","display_name":"Yang Xiang","orcid":"https://orcid.org/0000-0001-6104-1875"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5014238603","display_name":"Buzhou Tang","orcid":"https://orcid.org/0000-0003-0271-8246"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Buzhou Tang","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":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7818","last_page":"7835"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11053","display_name":"Process Optimization and Integration","score":0.535099983215332,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11053","display_name":"Process Optimization and Integration","score":0.535099983215332,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11407","display_name":"Innovative Microfluidic and Catalytic Techniques Innovation","score":0.025599999353289604,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10461","display_name":"Gas Sensing Nanomaterials and Sensors","score":0.01979999989271164,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.28369998931884766},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2824999988079071},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.24770000576972961},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.23389999568462372},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.23000000417232513}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5059000253677368},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.28630000352859497},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.24770000576972961},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24500000476837158},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.23389999568462372},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.23000000417232513},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2287999987602234},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.22519999742507935}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.413","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.413","pdf_url":"https://aclanthology.org/2025.findings-emnlp.413.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: EMNLP 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.413","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.413","pdf_url":"https://aclanthology.org/2025.findings-emnlp.413.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: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G965288165","display_name":null,"funder_award_id":"62276082","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034587.pdf","grobid_xml":"https://content.openalex.org/works/W4416034587.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Chain-of-thought":[0],"(CoT)":[1],"distillation":[2,62],"aims":[3],"to":[4,38,63,74,82,91,140],"enhance":[5,127],"small":[6,119],"language":[7],"models'":[8],"(SLMs)":[9],"reasoning":[10,14,129],"by":[11,108],"transferring":[12],"multi-step":[13],"capability":[15],"from":[16],"the":[17,39,43,76,79,84,92,122,128,135],"larger":[18],"teacher":[19],"models.However,":[20],"existing":[21],"work":[22],"underestimates":[23],"rationale":[24],"quality,":[25],"focusing":[26],"primarily":[27],"on":[28,99],"data":[29],"quantity,":[30],"which":[31,53],"may":[32],"transfer":[33],"noisy":[34],"or":[35],"incorrect":[36],"information":[37],"student":[40,80,132],"model.To":[41],"address":[42],"above":[44],"issues,":[45],"we":[46,94],"proposed":[47],"Model-Oriented":[48],"Rationale":[49,70],"Selection":[50],"Distillation":[51],"(MoRSD),":[52],"can":[54,126],"discern":[55],"and":[56,113],"select":[57],"high":[58,123],"quality":[59,124],"rationales":[60,107,125],"for":[61,145],"improve":[64],"performance":[65],"further.We":[66],"further":[67],"propose":[68],"a":[69,88,118,142],"Difficulty":[71],"(RD)":[72],"metric":[73],"measure":[75],"ability":[77,130],"of":[78,121,131],"model":[81],"generate":[83],"correct":[85],"answer":[86],"under":[87],"given":[89],"rationale.Compared":[90],"baseline,":[93],"achieved":[95],"4.6%":[96],"average":[97],"improvement":[98],"seven":[100],"datasets":[101],"over":[102],"three":[103],"tasks,":[104],"using":[105],"fewer":[106],"controlling":[109],"their":[110],"accuracy,":[111],"diversity,":[112],"difficulty.Our":[114],"results":[115],"reveal":[116],"that":[117],"portion":[120],"models":[133],"than":[134],"entire":[136],"dataset.Our":[137],"method":[138],"promises":[139],"be":[141],"possible":[143],"solution":[144],"efficient":[146],"CoT":[147],"distillation.":[148]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}
