{"id":"https://openalex.org/W4416033953","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1078","title":"Length Representations in Large Language Models","display_name":"Length Representations in Large Language Models","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416033953","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1078"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.1078","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1078","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1078.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.1078.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sangjun Moon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sangjun Moon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088337044","display_name":"Dasom Choi","orcid":"https://orcid.org/0000-0002-3392-6243"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dasom Choi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028543952","display_name":"Jingun Kwon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingun Kwon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016936747","display_name":"Hidetaka Kamigaito","orcid":"https://orcid.org/0000-0002-5249-5813"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hidetaka Kamigaito","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5035876897","display_name":"Manabu Okumura","orcid":"https://orcid.org/0009-0001-7730-1536"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manabu Okumura","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":0.29598269,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"19775","last_page":"19793"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.08529999852180481,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.08529999852180481,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.06650000065565109,"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":0.06629999727010727,"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/sequence","display_name":"Sequence (biology)","score":0.742900013923645},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.6456000208854675},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5343000292778015},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.448199987411499},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3465999960899353},{"id":"https://openalex.org/keywords/internal-model","display_name":"Internal model","score":0.3312000036239624},{"id":"https://openalex.org/keywords/time-sequence","display_name":"Time sequence","score":0.3237999975681305}],"concepts":[{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.742900013923645},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.6456000208854675},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6190999746322632},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5343000292778015},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.46619999408721924},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43540000915527344},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C28427503","wikidata":"https://www.wikidata.org/wiki/Q13580300","display_name":"Internal model","level":3,"score":0.3312000036239624},{"id":"https://openalex.org/C3020136221","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time sequence","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30570000410079956},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2962000072002411},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.29429998993873596},{"id":"https://openalex.org/C2993724205","wikidata":"https://www.wikidata.org/wiki/Q315","display_name":"Human language","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.27869999408721924},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27709999680519104},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.26260000467300415}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.1078","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1078","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1078.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.1078","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1078","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1078.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/G1628181847","display_name":null,"funder_award_id":"RS\u20102022\u201000155857","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G4700831490","display_name":null,"funder_award_id":"2022-","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G4939975072","display_name":null,"funder_award_id":"RS-2022-00155857","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G6789273066","display_name":null,"funder_award_id":"RS-2022-00155857","funder_id":"https://openalex.org/F4320321204","funder_display_name":"Chungnam National University"}],"funders":[{"id":"https://openalex.org/F4320321204","display_name":"Chungnam National University","ror":"https://ror.org/0227as991"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416033953.pdf","grobid_xml":"https://content.openalex.org/works/W4416033953.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"have":[4,36,140],"shown":[5],"remarkable":[6],"capabilities":[7],"across":[8],"various":[9],"tasks,":[10],"that":[11,64,107,138],"are":[12,68],"learned":[13,141],"from":[14,113],"massive":[15],"amounts":[16],"of":[17,101,133],"text-based":[18],"data.Although":[19],"LLMs":[20,139],"can":[21,76,91],"control":[22,35,92],"output":[23,48,72,94,149],"sequence":[24,49,73,95],"length,":[25,74],"particularly":[26],"in":[27,58,70,79],"instruction-based":[28],"settings,":[29],"the":[30,55,88,93,99,102,129],"internal":[31,56,131,145],"mechanisms":[32,67,146],"behind":[33],"this":[34,40,134],"been":[37],"unexplored":[38],"yet.In":[39],"study,":[41],"we":[42,90],"provide":[43],"empirical":[44],"evidence":[45],"on":[46],"how":[47],"length":[50,96,108,150],"information":[51,109],"is":[52,110],"encoded":[53],"within":[54,87],"representations":[57],"LLMs.In":[59],"particular,":[60],"our":[61],"findings":[62,136],"show":[63],"multi-head":[65],"attention":[66],"critical":[69],"determining":[71],"which":[75],"be":[77],"adjusted":[78],"a":[80],"disentangled":[81,112],"manner.By":[82],"scaling":[83],"specific":[84],"hidden":[85,117],"units":[86,118],"model,":[89],"without":[97,151],"losing":[98],"informativeness":[100],"generated":[103],"text,":[104],"thereby":[105],"indicating":[106],"partially":[111],"semantic":[114],"information.Moreover,":[115],"some":[116],"become":[119,124],"increasingly":[120],"active":[121],"as":[122],"prompts":[123],"more":[125],"length-specific,":[126],"thus":[127],"reflecting":[128],"model's":[130],"awareness":[132],"attribute.Our":[135],"suggest":[137],"robust":[142],"and":[143],"adaptable":[144],"for":[147],"controlling":[148],"any":[152],"external":[153],"control.":[154]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-11-08T00:00:00"}