{"id":"https://openalex.org/W7157016684","doi":"https://doi.org/10.48550/arxiv.2604.23113","title":"Reducing Detail Hallucinations in Long-Context Regulatory Understanding via Targeted Preference Optimization","display_name":"Reducing Detail Hallucinations in Long-Context Regulatory Understanding via Targeted Preference Optimization","publication_year":2026,"publication_date":"2026-04-25","ids":{"openalex":"https://openalex.org/W7157016684","doi":"https://doi.org/10.48550/arxiv.2604.23113"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.23113","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23113","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.23113","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134808484","display_name":"Yang Liu","orcid":"https://orcid.org/0009-0005-1420-1828"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134778646","display_name":"Bin Chong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chong, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134814128","display_name":"Yuhan Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Yuhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134802389","display_name":"Chongyang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chongyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134808568","display_name":"Hao Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134786123","display_name":"Ziyi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ziyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044583334","display_name":"Jiayu Liang","orcid":"https://orcid.org/0000-0001-5092-6475"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Jiayu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134809943","display_name":"Ran Ran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ran, Ran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134778657","display_name":"Qian (Paleontologist) Li","orcid":"https://orcid.org/0009-0004-5372-2258"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134819477","display_name":"Kefu Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Kefu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.30889999866485596,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.30889999866485596,"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":0.10729999840259552,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.08760000020265579,"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/preference","display_name":"Preference","score":0.7236999869346619},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5863999724388123},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.41749998927116394},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.3952000141143799},{"id":"https://openalex.org/keywords/obligation","display_name":"Obligation","score":0.3492000102996826},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.3294000029563904}],"concepts":[{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.7236999869346619},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5863999724388123},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5817999839782715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5206999778747559},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.41749998927116394},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35740000009536743},{"id":"https://openalex.org/C2778447849","wikidata":"https://www.wikidata.org/wiki/Q2648051","display_name":"Obligation","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3239000141620636},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3131999969482422},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C3018824978","wikidata":"https://www.wikidata.org/wiki/Q2894891","display_name":"Error analysis","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.23113","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23113","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.23113","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23113","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7822551131248474,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"frequently":[4],"produce":[5],"\\emph{detail":[6],"hallucinations}":[7],"when":[8],"processing":[9],"long":[10],"regulatory":[11,56],"documents,":[12],"including":[13],"subtle":[14],"errors":[15],"in":[16,87],"threshold":[17],"values,":[18],"units,":[19],"scopes,":[20],"obligation":[21],"levels,":[22],"and":[23,47,58,122,151,156],"conditions":[24],"that":[25,82,131],"preserve":[26],"surface":[27],"plausibility":[28],"while":[29],"corrupting":[30],"safety-critical":[31],"parameters.":[32],"We":[33,74,98],"formalize":[34],"this":[35],"phenomenon":[36],"through":[37],"a":[38,50,77],"fine-grained":[39],"\\emph{Detail":[40],"Error":[41,136],"Taxonomy}":[42],"of":[43],"five":[44,148],"error":[45,149],"types":[46,150],"introduce":[48],"\\textbf{DetailBench},":[49],"benchmark":[51],"built":[52],"from":[53],"172":[54],"real":[55],"documents":[57,61],"150":[59],"synthetic":[60],"spanning":[62],"three":[63,125],"jurisdictions,":[64],"with":[65,143],"human-annotated":[66],"detail-level":[67],"ground":[68],"truth":[69],"comprising":[70],"13,000":[71],"preference":[72,79],"pairs.":[73],"propose":[75],"\\textbf{DetailDPO},":[76],"targeted":[78],"optimization":[80],"framework":[81],"constructs":[83],"contrastive":[84],"pairs":[85,107],"differing":[86],"exactly":[88],"one":[89],"detail":[90,105],"dimension,":[91],"concentrating":[92],"DPO":[93],"gradient":[94,109],"signal":[95],"on":[96,115],"detail-bearing~tokens.":[97],"provide":[99],"theoretical":[100],"analysis":[101],"showing":[102],"why":[103],"\\emph{minimal":[104],"perturbation}":[106],"yield":[108],"concentration":[110],"under":[111],"mild":[112],"assumptions.":[113],"Experiments":[114],"the":[116,134],"Qwen2.5":[117],"family":[118],"(7B,":[119],"14B,":[120],"72B)":[121],"Llama-3.1-8B":[123],"across":[124,146],"context-length":[126],"tiers":[127],"(8K--64K":[128],"tokens)":[129],"show":[130],"DetailDPO":[132],"reduces":[133],"Detail":[135],"Rate":[137],"by":[138],"42--61\\%":[139],"relative":[140],"to":[141,154],"baselines,":[142],"consistent":[144],"gains":[145],"all":[147],"cross-domain":[152],"transfer":[153],"financial":[155],"medical":[157],"documents.":[158]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-29T00:00:00"}
