{"id":"https://openalex.org/W4416034856","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.475","title":"CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs","display_name":"CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034856","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.475"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.475","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.475","pdf_url":"https://aclanthology.org/2025.findings-emnlp.475.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.475.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101641250","display_name":"Qi Xu","orcid":"https://orcid.org/0000-0002-0138-593X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102863825","display_name":"Qian Liu","orcid":"https://orcid.org/0000-0001-8307-9460"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hao Fei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062917240","display_name":"Hang Yu","orcid":"https://orcid.org/0000-0003-3444-9992"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hang Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102684501","display_name":"Shuhao Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuhao Guan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080250423","display_name":"Xiao Wei","orcid":"https://orcid.org/0009-0003-7647-6419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Wei","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":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8937","last_page":"8953"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13643","display_name":"Artificial Intelligence in Law","score":0.48559999465942383,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13643","display_name":"Artificial Intelligence in Law","score":0.48559999465942383,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.10760000348091125,"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/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.046300001442432404,"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/natural-language","display_name":"Natural language","score":0.3203999996185303},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.29269999265670776},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.28870001435279846},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2791000008583069},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.26260000467300415}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6119999885559082},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.5472999811172485},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42820000648498535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37790000438690186},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.475","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.475","pdf_url":"https://aclanthology.org/2025.findings-emnlp.475.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"},{"id":"pmh:oai:researchspace.auckland.ac.nz:2292/74559","is_oa":true,"landing_page_url":"https://hdl.handle.net/2292/74559","pdf_url":null,"source":{"id":"https://openalex.org/S7407055463","display_name":"ResearchSpace (University of Auckland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I154130895","host_organization_name":"University of Auckland","host_organization_lineage":["https://openalex.org/I154130895"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Item"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.475","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.475","pdf_url":"https://aclanthology.org/2025.findings-emnlp.475.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":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034856.pdf","grobid_xml":"https://content.openalex.org/works/W4416034856.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Most":[0],"of":[1,67,84,89,129,166,180],"the":[2,32,55,63,82,94,105,116,120,124,127,136,140,151,164],"existing":[3],"work":[4],"focuses":[5],"on":[6,157],"enablingLLMs":[7],"to":[8,15,26,53,61,77,99,111,149],"leverage":[9],"legal":[10,18,28,56,64,79,85,101,113,130,143],"rules":[11],"(e.g.,":[12],"law":[13],"articles)":[14],"tackle":[16],"complex":[17],"reasoning":[19,65],"tasks,":[20],"but":[21],"largely":[22],"overlooks":[23],"their":[24],"ability":[25,66],"understand":[27],"rules.To":[29],"better":[30],"evaluate":[31],"LLMs'":[33],"capabilities":[34],"in":[35,38],"this":[36,39],"regard,":[37],"work,":[40],"we":[41,69],"propose":[42,70],"a":[43,71,158],"new":[44],"challenge":[45],"task:":[46],"Legal":[47,95,137],"Paragraph":[48],"Prediction":[49],"(LPP),":[50],"which":[51],"aims":[52,98],"predict":[54],"paragraph":[57],"given":[58,133],"criminal":[59,134,141,182],"facts.Moreover,":[60],"enhance":[62],"LLMs,":[68],"novel":[72],"framework":[73],"CLEAR,":[74],"enabling":[75],"LLMs":[76],"analyze":[78,123],"cases":[80],"with":[81,126],"guidance":[83,128],"rule":[86,102,131,144],"insights.CLEAR":[87],"consists":[88],"four":[90],"key":[91],"components,":[92],"where":[93],"Rules":[96],"Retriever":[97],"retrieve":[100],"knowledge,":[103],"and":[104,146,171],"Rule":[106],"Insights":[107],"Generator":[108],"is":[109,184],"used":[110],"generate":[112],"insights":[114,132],"guiding":[115],"LLM's":[117],"reasoning,":[118],"then":[119],"Case":[121],"Analyzer":[122],"case":[125],"facts.Finally,":[135],"Reasoner":[138],"synthesizes":[139],"facts,":[142],"insights,":[145],"analysis":[147],"results":[148,162],"derive":[150],"final":[152],"decision.By":[153],"conducting":[154],"extensive":[155],"experiments":[156],"real-world":[159],"dataset,":[160],"experimental":[161],"validate":[163],"effectiveness":[165],"our":[167],"proposed":[168],"model.Our":[169],"codes":[170],"dataset":[172],"are":[173],"available":[174],"at":[175],"https://github.com/xuqi220/CLEAR.Warning!!!This":[176],"paper":[177],"contains":[178],"discussions":[179],"violent":[181],"activities.Discretion":[183],"advised.":[185]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-11-08T00:00:00"}
