{"id":"https://openalex.org/W4387114267","doi":"https://doi.org/10.48550/arxiv.2309.13064","title":"InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning","display_name":"InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning","publication_year":2023,"publication_date":"2023-09-15","ids":{"openalex":"https://openalex.org/W4387114267","doi":"https://doi.org/10.48550/arxiv.2309.13064"},"language":"en","primary_location":{"id":"pmh:oai:RePEc:arx:papers:2309.13064","doi":null,"is_oa":false,"landing_page_url":"http://arxiv.org/pdf/2309.13064","pdf_url":"http://arxiv.org/pdf/2309.13064","source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"preprint"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2309.13064","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yang, Yi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yang, Yi","raw_affiliation_strings":["Hong Kong University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077931422","display_name":"Yixuan Tang","orcid":"https://orcid.org/0000-0003-3766-0929","observed_orcids":["https://orcid.org/0000-0003-3766-0929"]},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Tang, Yixuan","raw_affiliation_strings":["Hong Kong University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090061579","display_name":"Kar Yan Tam","orcid":"https://orcid.org/0000-0003-3242-0184","observed_orcids":["https://orcid.org/0000-0003-3242-0184"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tam, Kar Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":158,"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/T10028","display_name":"Topic Modeling","score":0.9042999744415283,"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/T10028","display_name":"Topic Modeling","score":0.9042999744415283,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.061500001698732376,"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"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.008700000122189522,"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/instruction-tuning","display_name":"instruction tuning","score":0.9369999766349792},{"id":"https://openalex.org/keywords/investment-advice","display_name":"investment advice","score":0.8949999809265137},{"id":"https://openalex.org/keywords/sec-filings","display_name":"SEC filings","score":0.9980000257492065},{"id":"https://openalex.org/keywords/mathematical-finance","display_name":"financial mathematics","score":0.7639999985694885},{"id":"https://openalex.org/keywords/zero-shot-evaluation","display_name":"zero-shot evaluation","score":0.6629999876022339}],"concepts":[{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.6280171275138855},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.565159261226654},{"id":"https://openalex.org/C27548731","wikidata":"https://www.wikidata.org/wiki/Q88272","display_name":"Investment (military)","level":3,"score":0.5038523077964783},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.49516287446022034},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46763017773628235},{"id":"https://openalex.org/C23925645","wikidata":"https://www.wikidata.org/wiki/Q5449731","display_name":"Financial modeling","level":2,"score":0.4347136914730072},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4102700352668762},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3250856101512909},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2986753582954407},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.24923449754714966},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.16456690430641174},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.14119002223014832},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138496976","wikidata":"https://www.wikidata.org/wiki/Q175002","display_name":"Developmental psychology","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:RePEc:arx:papers:2309.13064","doi":null,"is_oa":false,"landing_page_url":"http://arxiv.org/pdf/2309.13064","pdf_url":"http://arxiv.org/pdf/2309.13064","source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"preprint"},{"id":"pmh:oai:arXiv.org:2309.13064","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.13064","pdf_url":"https://arxiv.org/pdf/2309.13064","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2309.13064","doi":"https://doi.org/10.48550/arxiv.2309.13064","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2309.13064","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2309.13064","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.13064","pdf_url":"https://arxiv.org/pdf/2309.13064","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387114267.pdf","grobid_xml":"https://content.openalex.org/works/W4387114267.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W3037252472","https://openalex.org/W3196731672","https://openalex.org/W3198002980","https://openalex.org/W3198243861","https://openalex.org/W3205068155","https://openalex.org/W4307310886","https://openalex.org/W4318908031","https://openalex.org/W4360836968","https://openalex.org/W4361866125","https://openalex.org/W4380352301","https://openalex.org/W4382998379","https://openalex.org/W4385570142","https://openalex.org/W4385572634","https://openalex.org/W4385572697","https://openalex.org/W7133189643"],"related_works":["https://openalex.org/W4387963447","https://openalex.org/W6968080514","https://openalex.org/W4380352301","https://openalex.org/W7161091952","https://openalex.org/W7166259264","https://openalex.org/W7167694304","https://openalex.org/W7134804671","https://openalex.org/W4406272066","https://openalex.org/W4406491431","https://openalex.org/W4399207330"],"abstract_inverted_index":{"We":[0,191],"present":[1],"a":[2,18,37,44,109,119,126,135,143,160,166],"new":[3],"financial":[4,25,48,72,112,168,176,189],"domain":[5,128,169],"large":[6],"language":[7],"model,":[8,146],"InvestLM,":[9],"tuned":[10,133],"on":[11,108,142],"LLaMA-65B":[12],"(Touvron":[13],"et":[14,31,156],"al.,":[15,32,157],"2023),":[16,33],"using":[17,134],"carefully":[19,139],"curated":[20,140],"instruction":[21,41],"dataset":[22],"related":[23,49,80],"to":[24,58,61,78,96,196],"investment.":[26],"Inspired":[27],"by":[28],"less-is-more-for-alignment":[29],"(Zhou":[30,155],"we":[34],"manually":[35],"curate":[36],"small":[38,136],"yet":[39],"diverse":[40],"dataset,":[42],"covering":[43],"wide":[45],"range":[46],"of":[47,98,111,138,188],"topics,":[50],"from":[51],"Chartered":[52],"Financial":[53,82],"Analyst":[54],"(CFA)":[55],"exam":[56],"questions":[57],"SEC":[59],"filings":[60],"Stackexchange":[62],"quantitative":[63],"finance":[64],"discussions.":[65],"InvestLM":[66],"shows":[67],"strong":[68,116],"capabilities":[69],"in":[70,174],"understanding":[71,175],"text":[73],"and":[74,88,104,178],"provides":[75],"helpful":[76,180],"responses":[77],"investment":[79,181],"questions.":[81],"experts,":[83],"including":[84],"hedge":[85],"fund":[86],"managers":[87],"research":[89,120,198],"analysts,":[90],"rate":[91],"InvestLM's":[92],"response":[93],"as":[94],"comparable":[95],"those":[97],"state-of-the-art":[99,167],"commercial":[100],"models":[101],"(GPT-3.5,":[102],"GPT-4":[103],"Claude-2).":[105],"Zero-shot":[106],"evaluation":[107],"set":[110,137],"NLP":[113],"benchmarks":[114],"demonstrates":[115],"generalizability.":[117],"From":[118,159],"perspective,":[121,162],"this":[122,163],"work":[123,164,186],"suggests":[124],"that":[125],"high-quality":[127],"specific":[129],"LLM":[130,170],"can":[131],"be":[132],"instructions":[141],"well-trained":[144],"foundation":[145],"which":[147],"is":[148],"consistent":[149],"with":[150,171],"the":[151,185,193,197],"Superficial":[152],"Alignment":[153],"Hypothesis":[154],"2023).":[158],"practical":[161],"develops":[165],"superior":[172],"capability":[173],"texts":[177],"providing":[179],"advice,":[182],"potentially":[183],"enhancing":[184],"efficiency":[187],"professionals.":[190],"release":[192],"model":[194],"parameters":[195],"community.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":37},{"year":2025,"cited_by_count":69},{"year":2024,"cited_by_count":45},{"year":2023,"cited_by_count":7}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2023-09-29T00:00:00"}
