{"id":"https://openalex.org/W7133538630","doi":"https://doi.org/10.48550/arxiv.2603.03095","title":"Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection","display_name":"Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133538630","doi":"https://doi.org/10.48550/arxiv.2603.03095"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.03095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03095","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.03095","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128042297","display_name":"Sofiane Elguendouze","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elguendouze, Sofiane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107099115","display_name":"Erwan Hain","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hain, Erwan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123045800","display_name":"Elena Cabrio","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cabrio, Elena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128110291","display_name":"Serena Villata","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Villata, Serena","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":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.44830000400543213,"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/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.44830000400543213,"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":0.24040000140666962,"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":0.08179999887943268,"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/argumentative","display_name":"Argumentative","score":0.8634999990463257},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.7993999719619751},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5734999775886536},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5073000192642212},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.47780001163482666},{"id":"https://openalex.org/keywords/cognitive-reframing","display_name":"Cognitive reframing","score":0.4772999882698059},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.46880000829696655},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3544999957084656}],"concepts":[{"id":"https://openalex.org/C2781306805","wikidata":"https://www.wikidata.org/wiki/Q4789761","display_name":"Argumentative","level":2,"score":0.8634999990463257},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.7993999719619751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6315000057220459},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5734999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5618000030517578},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5073000192642212},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.47780001163482666},{"id":"https://openalex.org/C187029079","wikidata":"https://www.wikidata.org/wiki/Q958679","display_name":"Cognitive reframing","level":2,"score":0.4772999882698059},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47200000286102295},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3544999957084656},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35190001130104065},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2671000063419342},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.03095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03095","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.03095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03095","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7440149188041687}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Argumentative":[0],"component":[1,60,66],"detection":[2],"(ACD)":[3],"is":[4,134],"a":[5,55,63,76,94,145],"core":[6],"subtask":[7,40],"of":[8,14,65,130,136,151],"Argument(ation)":[9],"Mining":[10],"(AM)":[11],"and":[12,26,34,90],"one":[13,135],"its":[15],"most":[16,49],"challenging":[17],"aspects,":[18],"as":[19,32,54,93,144],"it":[20,53],"requires":[21],"jointly":[22],"delimiting":[23],"argumentative":[24],"spans":[25],"classifying":[27],"them":[28],"into":[29],"components":[30],"such":[31],"claims":[33],"premises.":[35],"While":[36],"research":[37],"on":[38,80,109,113],"this":[39,72,133],"remains":[41],"relatively":[42],"limited":[43],"compared":[44,123],"to":[45,100,124,140],"other":[46],"AM":[47,156],"tasks,":[48],"existing":[50],"approaches":[51],"formulate":[52],"simplified":[56],"sequence":[57],"labeling":[58],"problem,":[59],"classification,":[61],"or":[62],"pipeline":[64],"segmentation":[67],"followed":[68],"by":[69],"classification.":[70],"In":[71],"paper,":[73],"we":[74],"propose":[75],"novel":[77],"approach":[78,119],"based":[79],"instruction-tuned":[81],"Large":[82],"Language":[83],"Models":[84],"(LLMs)":[85],"using":[86],"compact":[87],"instruction-based":[88],"prompts,":[89],"reframe":[91],"ACD":[92,143],"language":[95],"generation":[96],"task,":[97,147],"enabling":[98],"arguments":[99],"be":[101],"identified":[102],"directly":[103],"from":[104],"plain":[105],"text":[106],"without":[107],"relying":[108],"pre-segmented":[110],"components.":[111],"Experiments":[112],"standard":[114],"benchmarks":[115],"show":[116],"that":[117],"our":[118,131],"achieves":[120],"higher":[121],"performance":[122],"state-of-the-art":[125],"systems.":[126],"To":[127],"the":[128,137,149],"best":[129],"knowledge,":[132],"first":[138],"attempts":[139],"fully":[141],"model":[142],"generative":[146],"highlighting":[148],"potential":[150],"instruction":[152],"tuning":[153],"for":[154],"complex":[155],"problems.":[157]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-05T00:00:00"}
