{"id":"https://openalex.org/W4416251387","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227752","title":"GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effects","display_name":"GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effects","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251387","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227752"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227752","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/A5075898247","display_name":"Chengguang Gan","orcid":"https://orcid.org/0000-0001-8034-0993"},"institutions":[{"id":"https://openalex.org/I180203408","display_name":"Yokohama National University","ror":"https://ror.org/03zyp6p76","country_code":"JP","type":"education","lineage":["https://openalex.org/I180203408"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chengguang Gan","raw_affiliation_strings":["Yokohama National University,Graduate School of Environment and Information Sciences,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yokohama National University,Graduate School of Environment and Information Sciences,Japan","institution_ids":["https://openalex.org/I180203408"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003052424","display_name":"Qinghao Zhang","orcid":"https://orcid.org/0000-0001-8175-4266"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Qinghao Zhang","raw_affiliation_strings":["Pusan National University,Department of Information Convergence Engineering,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pusan National University,Department of Information Convergence Engineering,South Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074022712","display_name":"Tatsunori Mori","orcid":"https://orcid.org/0000-0003-0656-6518"},"institutions":[{"id":"https://openalex.org/I180203408","display_name":"Yokohama National University","ror":"https://ror.org/03zyp6p76","country_code":"JP","type":"education","lineage":["https://openalex.org/I180203408"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tatsunori Mori","raw_affiliation_strings":["Yokohama National University,Graduate School of Environment and Information Sciences,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yokohama National University,Graduate School of Environment and Information Sciences,Japan","institution_ids":["https://openalex.org/I180203408"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.673,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87506856,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"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":0.7024999856948853,"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":0.7024999856948853,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.09570000320672989,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.02810000069439411,"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/generalization","display_name":"Generalization","score":0.6762999892234802},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6608999967575073},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6586999893188477},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.6442000269889832},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5913000106811523},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5587999820709229},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5268999934196472},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.49309998750686646}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7906000018119812},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6762999892234802},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6608999967575073},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6586999893188477},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.6442000269889832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6326000094413757},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5913000106811523},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5849000215530396},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5587999820709229},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5268999934196472},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.49309998750686646},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.41029998660087585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35199999809265137},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C39608478","wikidata":"https://www.wikidata.org/wiki/Q5015979","display_name":"Cache language model","level":5,"score":0.2996000051498413},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C70777604","wikidata":"https://www.wikidata.org/wiki/Q257885","display_name":"Word order","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.2531999945640564},{"id":"https://openalex.org/C94922259","wikidata":"https://www.wikidata.org/wiki/Q33215","display_name":"Constructed language","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227752","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2019759670","https://openalex.org/W2020278455","https://openalex.org/W2107598941","https://openalex.org/W2127978399","https://openalex.org/W2144578941","https://openalex.org/W2265846598","https://openalex.org/W2296283641","https://openalex.org/W2937423263","https://openalex.org/W2991358176","https://openalex.org/W4380519739","https://openalex.org/W4382202688","https://openalex.org/W4387969507","https://openalex.org/W4403942906","https://openalex.org/W4404782964"],"related_works":[],"abstract_inverted_index":{"Information":[0,83],"Extraction":[1,84],"(IE)":[2],"stands":[3],"as":[4],"a":[5,24,31,108,119,124,204],"cornerstone":[6],"in":[7,61,77,141,155,186],"natural":[8],"language":[9],"processing,":[10],"traditionally":[11],"segmented":[12],"into":[13],"distinct":[14],"sub-tasks.":[15],"The":[16],"advent":[17],"of":[18,30,41,118,127,158,184],"Large":[19,85],"Language":[20,86],"Models":[21],"(LLMs)":[22],"heralds":[23],"paradigm":[25],"shift,":[26],"suggesting":[27],"the":[28,39,72,81,115,131,134,171,181,194],"feasibility":[29],"singular":[32,205],"model":[33,120],"addressing":[34],"multiple":[35],"IE":[36,50,128,198],"subtasks.":[37,129],"However,":[38],"efficacy":[40],"employing":[42,107],"LLMs":[43],"directly":[44],"trained":[45],"on":[46],"chat-based":[47],"data":[48],"for":[49,196],"tasks":[51,143],"is":[52],"considerably":[53],"subpar":[54],"when":[55],"juxtaposed":[56],"with":[57],"conventional":[58],"methods":[59],"employed":[60],"prior":[62],"studies.":[63],"In":[64],"order":[65],"to":[66,145,200],"address":[67],"this":[68],"limitation":[69],"and":[70,104,176,188],"harness":[71],"robust":[73],"generalization":[74],"capabilities":[75],"inherent":[76],"LLMs,":[78],"we":[79],"propose":[80],"General":[82],"Model":[87],"(GIELLM).":[88],"GIELLM":[89,132],"seamlessly":[90],"integrates":[91],"various":[92],"tasks,":[93],"including":[94],"Text":[95,173],"Classification,":[96],"Sentiment":[97],"Analysis,":[98],"Named":[99],"Entity":[100],"Recognition,":[101],"Relation":[102,175],"Extraction,":[103,106],"Event":[105,177],"unified":[109],"input-output":[110],"schema.":[111],"This":[112,191],"innovation":[113],"marks":[114],"first":[116],"instance":[117],"simultaneously":[121],"handling":[122],"such":[123],"diverse":[125],"array":[126],"Notably,":[130],"leverages":[133],"Mutual":[135],"Reinforcement":[136],"Effect":[137],"(MRE),":[138],"enhancing":[139],"performance":[140],"integrated":[142],"compared":[144],"their":[146],"isolated":[147],"counterparts.":[148],"Our":[149],"experiments":[150],"demonstrate":[151],"State-of-the-Art":[152],"(SOTA)":[153],"results":[154],"five":[156],"out":[157],"six":[159],"Japanese":[160],"mixed":[161],"datasets,":[162],"significantly":[163],"surpassing":[164],"GPT-3.5-Turbo.":[165],"Further,":[166],"an":[167],"independent":[168],"evaluation":[169],"using":[170],"novel":[172],"Classification":[174],"Extraction(TCREE)":[178],"dataset":[179],"corroborates":[180],"synergistic":[182],"advantages":[183],"MRE":[185],"text":[187],"word":[189],"classification.":[190],"breakthrough":[192],"paves":[193],"way":[195],"most":[197],"subtasks":[199],"be":[201],"subsumed":[202],"under":[203],"LLM":[206],"framework.":[207],"Specialized":[208],"fine-tune":[209],"task-specific":[210],"models":[211],"are":[212],"no":[213],"longer":[214],"needed.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-14T00:00:00"}
