{"id":"https://openalex.org/W4412888278","doi":"https://doi.org/10.18653/v1/2025.findings-acl.641","title":"Quantile Regression with Large Language Models for Price Prediction","display_name":"Quantile Regression with Large Language Models for Price Prediction","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412888278","doi":"https://doi.org/10.18653/v1/2025.findings-acl.641"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.findings-acl.641","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.641","pdf_url":"https://aclanthology.org/2025.findings-acl.641.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: ACL 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-acl.641.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080114650","display_name":"Nikhita Vedula","orcid":"https://orcid.org/0000-0003-4857-5308"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nikhita Vedula","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007853790","display_name":"Dushyanta Dhyani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dushyanta Dhyani","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030483392","display_name":"Laleh Jalali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laleh Jalali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012459174","display_name":"Boris N. Oreshkin","orcid":"https://orcid.org/0000-0002-1869-2004"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boris N. Oreshkin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091599934","display_name":"Mohsen Bayati","orcid":"https://orcid.org/0000-0002-7280-912X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohsen Bayati","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059403175","display_name":"Shervin Malmasi","orcid":"https://orcid.org/0000-0001-6250-5571"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shervin Malmasi","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":"12396","last_page":"12415"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.6869000196456909,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.6869000196456909,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.7634458541870117},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6732248067855835},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.45768582820892334},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4526722729206085},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.441017210483551},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4231144189834595},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4093499183654785},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.28644928336143494},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18552961945533752}],"concepts":[{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.7634458541870117},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6732248067855835},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.45768582820892334},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4526722729206085},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.441017210483551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4231144189834595},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4093499183654785},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28644928336143494},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18552961945533752}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-acl.641","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.641","pdf_url":"https://aclanthology.org/2025.findings-acl.641.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: ACL 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-acl.641","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.641","pdf_url":"https://aclanthology.org/2025.findings-acl.641.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: ACL 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412888278.pdf","grobid_xml":"https://content.openalex.org/works/W4412888278.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4206618949","https://openalex.org/W2526321210","https://openalex.org/W3205863630","https://openalex.org/W4318833145","https://openalex.org/W2364275385","https://openalex.org/W4388704167","https://openalex.org/W2007977664","https://openalex.org/W4376874882","https://openalex.org/W2224749288","https://openalex.org/W4252013513"],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4],"shown":[5],"promise":[6],"in":[7,149],"structured":[8],"prediction":[9,39,80,110],"tasks,":[10],"including":[11],"regression,":[12],"but":[13],"existing":[14],"approaches":[15,95],"primarily":[16],"focus":[17],"on":[18],"point":[19,72,98],"estimates":[20],"and":[21,50,99,112,124,136],"lack":[22],"systematic":[23,115,154],"comparison":[24,116],"across":[25,76],"different":[26],"methods.We":[27],"investigate":[28],"probabilistic":[29],"regression":[30,59],"using":[31],"LLMs":[32,63],"for":[33,96,109],"unstructured":[34],"inputs,":[35],"addressing":[36],"challenging":[37],"text-to-distribution":[38],"tasks":[40],"such":[41],"as":[42,102],"price":[43,79],"estimation":[44],"where":[45],"both":[46,97],"nuanced":[47],"text":[48],"understanding":[49],"uncertainty":[51],"quantification":[52],"are":[53,158],"critical.We":[54],"propose":[55],"a":[56,85],"novel":[57],"quantile":[58,90],"approach":[60],"that":[61,84,128],"enables":[62],"to":[64,162],"produce":[65],"full":[66],"predictive":[67],"distributions,":[68],"improving":[69],"upon":[70],"traditional":[71,94],"estimates.Through":[73],"extensive":[74],"experiments":[75,140],"three":[77,105],"diverse":[78],"datasets,":[81],"we":[82],"demonstrate":[83],"Mistral-7B":[86,129],"model":[87,120],"fine-tuned":[88],"with":[89],"heads":[91],"significantly":[92],"outperforms":[93,131],"distributional":[100,113],"estimations,":[101],"measured":[103],"by":[104],"established":[106],"metrics":[107],"each":[108],"accuracy":[111,152],"calibration.Our":[114],"of":[117,145],"LLM":[118],"approaches,":[119,123],"architectures,":[121,133],"training":[122],"data":[125],"scaling":[126],"reveals":[127],"consistently":[130],"encoder":[132],"embedding-based":[134],"methods,":[135],"few-shot":[137],"learning":[138],"methods.Our":[139],"also":[141],"reveal":[142],"the":[143],"effectiveness":[144],"LLM-assisted":[146],"label":[147],"correction":[148],"achieving":[150],"human-level":[151],"without":[153],"bias.Our":[155],"curated":[156],"datasets":[157],"made":[159],"available":[160],"1":[161],"support":[163],"future":[164],"research.":[165]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}