FinanceLLM: A Survey of Large Language Models in Finance

Ojas Nimase; Zhengao Li; Xinyu Wei; Jinglin Hu; Luojia Liu; Kangyi Zhao; Yue Zhao; Yushun Dong

FinanceLLM: A Survey of Large Language Models in Finance

Ojas Nimase, Zhengao Li, Xinyu Wei, Jinglin Hu, Luojia Liu, Kangyi Zhao, Yue Zhao, Yushun Dong

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

Large Language Models (LLMs) have emerged as transformative, powerful tools for processing and acting on unstructured financial data. However, existing surveys suffer from methodological fragmentation, evaluation inconsistency, and application disconnect. This hinders the transition of research from academia to real-life applications in financial institutions. To address these gaps, we (1) introduce a unified taxonomy organizing approaches by computational complexity, (2) propose a three-stage evaluation framework, and (3) examine LLM deployment approaches suited to specific operational contexts. Furthermore, we identify key challenges including validation frameworks, data contamination, and operational compliance that need to be further investigated.

Anthology ID:: 2026.findings-emnlp.2265
Volume:: Findings of the Association for Computational Linguistics: EMNLP 2026
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: Findings
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 48501–48514
Language:
URL:: https://aclanthology.org/2026.findings-emnlp.2265/
DOI:
Bibkey:
Cite (ACL):: Ojas Nimase, Zhengao Li, Xinyu Wei, Jinglin Hu, Luojia Liu, Kangyi Zhao, Yue Zhao, and Yushun Dong. 2026. FinanceLLM: A Survey of Large Language Models in Finance. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 48501–48514, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: FinanceLLM: A Survey of Large Language Models in Finance (Nimase et al., Findings 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.findings-emnlp.2265.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{nimase-etal-2026-financellm,
    title = "{F}inance{LLM}: A Survey of Large Language Models in Finance",
    author = "Nimase, Ojas  and
      Li, Zhengao  and
      Wei, Xinyu  and
      Hu, Jinglin  and
      Liu, Luojia  and
      Zhao, Kangyi  and
      Zhao, Yue  and
      Dong, Yushun",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.2265/",
    pages = "48501--48514",
    ISBN = "979-8-89176-462-0",
    abstract = "Large Language Models (LLMs) have emerged as transformative, powerful tools for processing and acting on unstructured financial data. However, existing surveys suffer from methodological fragmentation, evaluation inconsistency, and application disconnect. This hinders the transition of research from academia to real-life applications in financial institutions. To address these gaps, we (1) introduce a unified taxonomy organizing approaches by computational complexity, (2) propose a three-stage evaluation framework, and (3) examine LLM deployment approaches suited to specific operational contexts. Furthermore, we identify key challenges including validation frameworks, data contamination, and operational compliance that need to be further investigated."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nimase-etal-2026-financellm">
    <titleInfo>
        <title>FinanceLLM: A Survey of Large Language Models in Finance</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ojas</namePart>
        <namePart type="family">Nimase</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhengao</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xinyu</namePart>
        <namePart type="family">Wei</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jinglin</namePart>
        <namePart type="family">Hu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Luojia</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kangyi</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yue</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yushun</namePart>
        <namePart type="family">Dong</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sunipa</namePart>
            <namePart type="family">Dev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Desmond</namePart>
            <namePart type="family">Elliott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-462-0</identifier>
    </relatedItem>
    <abstract>Large Language Models (LLMs) have emerged as transformative, powerful tools for processing and acting on unstructured financial data. However, existing surveys suffer from methodological fragmentation, evaluation inconsistency, and application disconnect. This hinders the transition of research from academia to real-life applications in financial institutions. To address these gaps, we (1) introduce a unified taxonomy organizing approaches by computational complexity, (2) propose a three-stage evaluation framework, and (3) examine LLM deployment approaches suited to specific operational contexts. Furthermore, we identify key challenges including validation frameworks, data contamination, and operational compliance that need to be further investigated.</abstract>
    <identifier type="citekey">nimase-etal-2026-financellm</identifier>
    <location>
        <url>https://aclanthology.org/2026.findings-emnlp.2265/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>48501</start>
            <end>48514</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T FinanceLLM: A Survey of Large Language Models in Finance
%A Nimase, Ojas
%A Li, Zhengao
%A Wei, Xinyu
%A Hu, Jinglin
%A Liu, Luojia
%A Zhao, Kangyi
%A Zhao, Yue
%A Dong, Yushun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F nimase-etal-2026-financellm
%X Large Language Models (LLMs) have emerged as transformative, powerful tools for processing and acting on unstructured financial data. However, existing surveys suffer from methodological fragmentation, evaluation inconsistency, and application disconnect. This hinders the transition of research from academia to real-life applications in financial institutions. To address these gaps, we (1) introduce a unified taxonomy organizing approaches by computational complexity, (2) propose a three-stage evaluation framework, and (3) examine LLM deployment approaches suited to specific operational contexts. Furthermore, we identify key challenges including validation frameworks, data contamination, and operational compliance that need to be further investigated.
%U https://aclanthology.org/2026.findings-emnlp.2265/
%P 48501-48514

Download as File

Markdown (Informal)

[FinanceLLM: A Survey of Large Language Models in Finance](https://aclanthology.org/2026.findings-emnlp.2265/) (Nimase et al., Findings 2026)

FinanceLLM: A Survey of Large Language Models in Finance (Nimase et al., Findings 2026)

ACL

Ojas Nimase, Zhengao Li, Xinyu Wei, Jinglin Hu, Luojia Liu, Kangyi Zhao, Yue Zhao, and Yushun Dong. 2026. FinanceLLM: A Survey of Large Language Models in Finance. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 48501–48514, Budapest, Hungary. Association for Computational Linguistics.