{"id":"https://openalex.org/W4405490733","doi":"https://doi.org/10.1109/iccp63557.2024.10793048","title":"Exploring the Limits of T5: Multitask Fine- Tuning for Reasoning, Summarization, Sentiment Analysis and Figurative Language Understanding","display_name":"Exploring the Limits of T5: Multitask Fine- Tuning for Reasoning, Summarization, Sentiment Analysis and Figurative Language Understanding","publication_year":2024,"publication_date":"2024-10-17","ids":{"openalex":"https://openalex.org/W4405490733","doi":"https://doi.org/10.1109/iccp63557.2024.10793048"},"language":"en","primary_location":{"id":"doi:10.1109/iccp63557.2024.10793048","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp63557.2024.10793048","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Intelligent Computer Communication and Processing (ICCP)","raw_type":"proceedings-article"},"type":"article","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/A5115513786","display_name":"Stefan Costin Atitienei","orcid":null},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Stefan Costin Atitienei","raw_affiliation_strings":["University of Cluj Napoca,Department of Computer Science Technical,Cluj Napoca,Romania"],"affiliations":[{"raw_affiliation_string":"University of Cluj Napoca,Department of Computer Science Technical,Cluj Napoca,Romania","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085755068","display_name":"Anca Marginean","orcid":"https://orcid.org/0000-0001-8426-588X"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Anca Marginean","raw_affiliation_strings":["University of Cluj Napoca,Department of Computer Science Technical,Cluj Napoca,Romania"],"affiliations":[{"raw_affiliation_string":"University of Cluj Napoca,Department of Computer Science Technical,Cluj Napoca,Romania","institution_ids":["https://openalex.org/I158333966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5115513786"],"corresponding_institution_ids":["https://openalex.org/I158333966"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21454711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"08"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.6672999858856201,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.6672999858856201,"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":0.628600001335144,"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":0.5835000276565552,"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/automatic-summarization","display_name":"Automatic summarization","score":0.9084720611572266},{"id":"https://openalex.org/keywords/literal-and-figurative-language","display_name":"Literal and figurative language","score":0.8472728729248047},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7728891372680664},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6834043860435486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.587878406047821},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.556906521320343},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.32240307331085205}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9084720611572266},{"id":"https://openalex.org/C46182478","wikidata":"https://www.wikidata.org/wiki/Q7363315","display_name":"Literal and figurative language","level":2,"score":0.8472728729248047},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7728891372680664},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6834043860435486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.587878406047821},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.556906521320343},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.32240307331085205},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccp63557.2024.10793048","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp63557.2024.10793048","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Intelligent Computer Communication and Processing (ICCP)","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":10,"referenced_works":["https://openalex.org/W1840435438","https://openalex.org/W2251939518","https://openalex.org/W2979826702","https://openalex.org/W4285105218","https://openalex.org/W6605323724","https://openalex.org/W6703902474","https://openalex.org/W6750615492","https://openalex.org/W6754144339","https://openalex.org/W6755207826","https://openalex.org/W6769627184"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W4389760904","https://openalex.org/W2150160875","https://openalex.org/W4242223894","https://openalex.org/W4306886878","https://openalex.org/W2973759123","https://openalex.org/W1517524280"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,101],"comprehensive":[4],"study":[5],"on":[6,50,94,105,186],"fine-tuning":[7,56,103,184],"the":[8,64,72,92,109,116,131,149,180,187],"T5-small":[9],"model":[10,52,93,132],"across":[11],"multiple":[12,67],"natural":[13],"language":[14,121],"processing":[15],"(NLP)":[16],"tasks":[17],"using":[18,122],"diverse":[19],"datasets,":[20],"including":[21],"SNLI":[22,162],"(Stanford":[23,33],"Natural":[24],"Language":[25,45],"Inference),":[26],"MRPC":[27,106],"(Microsoft":[28],"Research":[29],"Paraphrase":[30],"Corpus),":[31],"SST2":[32],"Sentiment":[34],"Tree-bank),":[35],"XSUM":[36],"(Extreme":[37],"Summarization),":[38],"and":[39,42,58,128,163,166,173],"IMPLI":[40],"(Idiomatic":[41],"Metaphoric":[43],"Paired":[44],"Inference).":[46],"Our":[47,141],"experiments":[48],"focus":[49],"optimizing":[51],"performance":[53],"through":[54],"various":[55,144],"strategies":[57,194],"parameter":[59],"adjustments.":[60],"Initially,":[61],"we":[62,90,124],"explored":[63],"use":[65],"of":[66,74,111,119,182],"prefixes":[68],"in":[69,137],"comparison":[70],"to":[71,77,107,133,147],"introduction":[73],"special":[75],"tokens":[76],"determine":[78],"which":[79],"strategy":[80],"enhances":[81],"input":[82],"flexibility":[83],"more":[84],"effectively,":[85],"facilitating":[86],"user-specific":[87],"customization.":[88],"Then,":[89],"fine-tuned":[91,130],"all":[95],"datasets":[96],"except":[97],"MRPC,":[98,169],"followed":[99],"by":[100],"subsequent":[102],"exclusively":[104],"investigate":[108],"phenomenon":[110],"catastrophic":[112],"forgetting.":[113],"To":[114],"evaluate":[115],"model's":[117,150,188],"understanding":[118],"figurative":[120,139],"IMPLI,":[123],"conducted":[125],"specific":[126],"tests":[127],"subsequently":[129],"improve":[134],"its":[135],"reliability":[136],"handling":[138],"language.":[140],"evaluation":[142],"encompassed":[143],"generation":[145],"techniques":[146],"assess":[148],"behavior":[151],"post-fine-tuning.":[152],"We":[153],"employed":[154],"distinct":[155],"metrics":[156],"for":[157,161,168,171,175,195],"each":[158],"task:":[159],"accuracy":[160,165,174],"SST2,":[164],"F1":[167],"ROUGE":[170],"XSUM,":[172],"IMPLI.":[176],"The":[177],"results":[178],"demonstrate":[179],"impact":[181],"different":[183],"methodologies":[185],"performance,":[189],"providing":[190],"insights":[191],"into":[192],"effective":[193],"multi-task":[196],"learning":[197],"with":[198],"T5-small.":[199]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
