{"id":"https://openalex.org/W4416034958","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.407","title":"Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs","display_name":"Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034958","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.407"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.407","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.407","pdf_url":"https://aclanthology.org/2025.findings-emnlp.407.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.407.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008489037","display_name":"Yunzhe Qi","orcid":"https://orcid.org/0000-0001-5828-7436"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yunzhe Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031899538","display_name":"Jinjin Tian","orcid":"https://orcid.org/0000-0003-3537-6430"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinjin Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035373409","display_name":"T. Liu","orcid":"https://orcid.org/0000-0002-8396-8564"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianci Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101737630","display_name":"Ruirui Li","orcid":"https://orcid.org/0000-0002-8014-7170"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruirui Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024869027","display_name":"Tianxin Wei","orcid":"https://orcid.org/0000-0003-4450-2005"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianxin Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118844279","display_name":"Hui Liu","orcid":"https://orcid.org/0000-0002-0576-2915"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hui Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102008510","display_name":"Xianfeng Tang","orcid":"https://orcid.org/0000-0003-1554-2761"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xianfeng Tang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110641878","display_name":"Monica Xiao Cheng","orcid":"https://orcid.org/0009-0003-8594-7104"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Monica Xiao Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5003062659","display_name":"Jingrui He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingrui He","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35115262,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7707","last_page":"7733"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.15160000324249268,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.15160000324249268,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10715","display_name":"Distributed and Parallel Computing Systems","score":0.08229999989271164,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.0421999990940094,"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/key","display_name":"Key (lock)","score":0.32260000705718994},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.24300000071525574},{"id":"https://openalex.org/keywords/hybrid-learning","display_name":"Hybrid learning","score":0.24089999496936798},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.23890000581741333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5194000005722046},{"id":"https://openalex.org/C145420912","wikidata":"https://www.wikidata.org/wiki/Q853077","display_name":"Mathematics education","level":1,"score":0.3919000029563904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3231000006198883},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.32089999318122864},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.24899999797344208},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.24300000071525574},{"id":"https://openalex.org/C3018790387","wikidata":"https://www.wikidata.org/wiki/Q869010","display_name":"Hybrid learning","level":2,"score":0.24089999496936798},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2402999997138977},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.23890000581741333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.407","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.407","pdf_url":"https://aclanthology.org/2025.findings-emnlp.407.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.407","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.407","pdf_url":"https://aclanthology.org/2025.findings-emnlp.407.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/G1957999595","display_name":null,"funder_award_id":"#2020-67021-32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G2926720356","display_name":null,"funder_award_id":"32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G4233114941","display_name":null,"funder_award_id":"1024178","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G5620962805","display_name":null,"funder_award_id":"67021","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"}],"funders":[{"id":"https://openalex.org/F4320306114","display_name":"U.S. Department of Agriculture","ror":"https://ror.org/01na82s61"},{"id":"https://openalex.org/F4320332299","display_name":"National Institute of Food and Agriculture","ror":"https://ror.org/05qx3fv49"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034958.pdf","grobid_xml":"https://content.openalex.org/works/W4416034958.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"performance":[1],"of":[2,106],"Large":[3],"Language":[4],"Models":[5],"(LLMs)":[6],"critically":[7],"depends":[8],"on":[9],"designing":[10],"effective":[11,62],"instructions,":[12],"which":[13,128],"is":[14],"particularly":[15],"challenging":[16],"for":[17,43,134,160],"black-box":[18,101,108,131],"LLMs":[19],"with":[20],"inaccessible":[21,107],"internal":[22],"states.To":[23],"this":[24,65],"end,":[25],"we":[26,67],"introduce":[27],"Learning":[28,152],"to":[29,58,87,153],"Instruct,":[30],"a":[31,44,73,78,82,116,156],"novel":[32,74,117],"paradigm":[33],"that":[34,76,140],"formulates":[35],"instruction":[36,63,84,163],"optimization":[37],"as":[38,155],"an":[39],"LLM":[40,80,132,162],"fine-tuning":[41],"objective":[42],"white-box":[45,79],"\"instruction":[46],"engineer\"":[47],"LLM,":[48],"leveraging":[49],"its":[50],"rich":[51],"learning":[52],"capacity":[53],"and":[54,61,96,110,149],"vast":[55],"pre-trained":[56],"knowledge":[57],"enable":[59],"efficient":[60],"optimization.Within":[64],"paradigm,":[66],"propose":[68],"Automatic":[69],"Instruction":[70],"Optimizer":[71],"(AIO),":[72],"framework":[75],"fine-tunes":[77],"into":[81],"capable":[83],"engineer.AIO":[85],"learns":[86],"optimize":[88],"taskaware,":[89],"human-comprehensible":[90],"instructions":[91],"by":[92,124],"incorporating":[93],"task":[94],"nuances":[95],"feedback":[97,133],"from":[98],"the":[99,104],"task-solving":[100],"LLM.To":[102],"overcome":[103],"challenges":[105],"gradients":[109],"high":[111],"API":[112],"costs,":[113],"AIO":[114,141],"introduces":[115],"zeroth-order":[118],"(ZO)":[119],"gradient":[120],"approximation":[121],"mechanism":[122],"guided":[123],"Thompson":[125],"Sampling":[126],"(TS),":[127],"reuses":[129],"informative":[130],"improved":[135],"query":[136],"efficiency.Extensive":[137],"experiments":[138],"show":[139],"generally":[142],"outperforms":[143],"strong":[144],"baselines":[145],"in":[146],"both":[147],"effectiveness":[148],"efficiency,":[150],"establishing":[151],"Instruct":[154],"promising":[157],"new":[158],"direction":[159],"blackbox":[161],"optimization.":[164]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-11-08T00:00:00"}
