{"id":"https://openalex.org/W4416251365","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228874","title":"Similarity-based Prompt Optimization for Distilling Factual Knowledge From Language Models","display_name":"Similarity-based Prompt Optimization for Distilling Factual Knowledge From Language Models","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251365","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228874"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228874","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228874","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028866522","display_name":"Na Li","orcid":"https://orcid.org/0009-0000-6776-3908"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Na Li","raw_affiliation_strings":["University of Shanghai for Science and Technology,School of Optical-Electrical and Computer Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Shanghai for Science and Technology,School of Optical-Electrical and Computer Engineering,China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100449224","display_name":"Haoran Liu","orcid":"https://orcid.org/0000-0002-1744-3436"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Liu","raw_affiliation_strings":["University of Shanghai for Science and Technology,School of Optical-Electrical and Computer Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Shanghai for Science and Technology,School of Optical-Electrical and Computer Engineering,China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071473857","display_name":"Dunlu Peng","orcid":"https://orcid.org/0000-0003-0223-5447"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dunlu Peng","raw_affiliation_strings":["University of Shanghai for Science and Technology,School of Optical-Electrical and Computer Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Shanghai for Science and Technology,School of Optical-Electrical and Computer Engineering,China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107179439","display_name":"Zied Bouraoui","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I44563897","display_name":"Universit\u00e9 d'Artois","ror":"https://ror.org/053x9s498","country_code":"FR","type":"education","lineage":["https://openalex.org/I44563897"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Zied Bouraoui","raw_affiliation_strings":["CRIL Univ-Artois,CNRS,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRIL Univ-Artois,CNRS,France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I44563897"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.6323000192642212,"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":0.6323000192642212,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06669999659061432,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.05050000175833702,"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/representation","display_name":"Representation (politics)","score":0.6096000075340271},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5895000100135803},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5805000066757202},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5242999792098999},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4050000011920929},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.30559998750686646}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516000270843506},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6096000075340271},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5895000100135803},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5805000066757202},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5242999792098999},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5238000154495239},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3677000105381012},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3434000015258789},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.289900004863739},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228874","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228874","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2911489562","https://openalex.org/W2970476646","https://openalex.org/W2991223644","https://openalex.org/W2998557616","https://openalex.org/W3044175177","https://openalex.org/W3044438666","https://openalex.org/W3098267758","https://openalex.org/W3100452049","https://openalex.org/W3102659883","https://openalex.org/W3154903254","https://openalex.org/W3166846774","https://openalex.org/W3172335055","https://openalex.org/W3174770825","https://openalex.org/W4205450747","https://openalex.org/W4280545882","https://openalex.org/W4285247752","https://openalex.org/W4287888678","https://openalex.org/W4382982697","https://openalex.org/W4385573710","https://openalex.org/W4386065596","https://openalex.org/W4386187806"],"related_works":[],"abstract_inverted_index":{"Considerable":[0],"attention":[1],"has":[2],"been":[3],"devoted":[4],"recently":[5],"to":[6,40],"the":[7,26,30,42,58,69,76,80],"problem":[8],"of":[9,34,63],"exploring":[10],"and":[11,32,61,75,96],"distilling":[12],"factual":[13,111],"knowledge":[14],"from":[15],"Pre-trained":[16],"Language":[17],"Models":[18],"(PLMs).":[19],"While":[20],"continuous":[21,35,64],"prompts":[22],"are":[23],"currently":[24],"considered":[25],"best":[27,59],"approach,":[28],"determining":[29],"number":[31,62],"positions":[33,60],"prompt":[36,65,73,85],"vectors":[37,66],"remains":[38],"challenging":[39],"obtain":[41],"optimal":[43,82],"prompts.":[44,83],"In":[45],"this":[46],"paper,":[47],"we":[48],"introduce":[49],"a":[50],"new":[51],"similarity-based":[52],"algorithm":[53],"that":[54,102],"automatically":[55],"searches":[56],"for":[57,79],"based":[67],"on":[68],"similarity":[70],"score":[71],"between":[72],"representation":[74,78],"relation":[77],"final":[81],"The":[84],"optimization":[86],"process":[87],"is":[88],"done":[89],"before":[90],"model":[91],"training,":[92],"making":[93],"it":[94],"efficient":[95],"less":[97],"time-consuming.":[98],"Our":[99],"experiments":[100],"demonstrate":[101],"our":[103],"method":[104],"significantly":[105],"outperforms":[106],"state-of-the-art":[107],"methods":[108],"in":[109],"probing":[110],"knowledge.":[112]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-11-14T00:00:00"}