A Report on Financial Regulations Challenge at COLING 2025

Alex Lin; Andy Zhu; Cailean Osborne; Charlie Shen; Daniel Kim; Jaisal Patel; Kairong Xiao; Keyi Wang; Luca Borella; Matt White

REVIEW 4 major objections 6 minor 1 cited by

A Report on Financial Regulations Challenge at COLING 2025

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that a nine-task Regulations Challenge provides a valid measure of LLM performance on financial regulation, and reports that submitted models answer regulatory Q&A well but fail on abbreviations, link retrieval, and XBRL…

desk verdict A transparent but unvalidated shared-task report; the private certificate answer key carries 20% of the final weight and is the main reason to be cautious about the leaderboard. read the letter →

arxiv 2412.11159 v2 pith:5JTGLRCE submitted 2024-12-15 cs.CE

Keyi Wang , Jaisal Patel , Charlie Shen , Daniel Kim , Andy Zhu , Alex Lin , Luca Borella , Cailean Osborne

show 4 more authors

Matt White Steve Yang Kairong Xiao Xiao-Yang Liu Yanglet

This is my paper · ORCID

classification cs.CE

keywords financialregulationslargelanguagemodelssharedtaskbenchmarkXBRLanalyticscertificateexamsabbreviationrecognitionevaluationmetrics

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reports the Regulations Challenge, a shared task the authors organized to measure whether large language models can handle financial regulations, and presents the resulting leaderboard. The organizers built nine question sets spanning basic regulation literacy, CFA and CPA certificate questions, the Common Domain Model, the Model Openness Framework, and XBRL filings, and scored six submitted systems against three baselines. The paper's claim is that these nine tasks form a valid evaluation of regulation-related capability, and the evidence shows a clear pattern: models answer factual and definitional questions well, but score poorly on abbreviation recognition, regulatory link retrieval, and XBRL tag queries. The winning submitted system scores 0.54801, below GPT-4o at 0.63567 and Mistral Large 2 at 0.62489, which the paper attributes to larger baseline models having more knowledge and reasoning capacity. If the evaluation is fair, the practical lesson is that today's LLMs are useful for regulatory question answering but are not yet reliable at identifier-level precision tasks such as tickers, license abbreviations, and exact citations.

What carries the argument

The central object is the nine-task question set with a weighted composite score. Tasks 1–5 (abbreviation, definition, NER, QA, and link retrieval) each count 10%; Task 6 (CFA and CPA certificate questions) counts 20%; and Tasks 7–9 (CDM, MOF licenses, and XBRL analytics) each count 10%. Answers are scored by accuracy for exact-answer tasks, F1 for NER, BERTScore for definition recognition, and FActScore for open-ended QA, producing the final score that drives the ranking.

What would settle it

A reader could take the six submitted systems and the three baselines, re-score them on a fresh, independently verified set of CFA/CPA, regulation, CDM, MOF, and XBRL questions using equal one-ninth weights for all nine tasks, and compare against a human expert answer key. If the top system changes or expert agreement with the reference labels is weak, the published ranking is an artifact of protocol choices rather than a measure of regulation expertise.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is a new benchmark plus a first measurement of where LLMs stand on financial regulation. The nine tasks cover information retrieval, certificate exams, the Common Domain Model, the Model Openness Framework, and XBRL analytics, with scores combined as 10% for each of Tasks 1–5, 20% for the certificate task, and 10% for each of Tasks 7–9. FinMind-Y-Me tops the submitted teams with 0.54801 by fine-tuning Qwen 2.5-7B-Instruct with sequential and reasoning-based training, yet both GPT-4o and Mistral Large 2 finish ahead. The consistent cross-model failures on abbreviations and link retrieval, and the zero NER scores of three submitted models, are presented as evidence of specific capability gaps rather than general uselessness.

Load-bearing premise

The ranking rests on the assumption that the hand-written reference answers are correct and unambiguous, the 10%/20%/10% task weights and random question subsets do not favor one model over another, and the models never memorized the test questions during pretraining.

Editorial extensions

If this is right

If the benchmark is accepted, LLMs are currently usable as regulatory Q&A assistants, since all systems scored above 0.6 on QA-style subtasks such as CDM and XBRL term explanation.
Abbreviation recognition is a reliable weakness, with no system exceeding 0.38 on the abbreviation task, so financial-domain tools need explicit ticker and acronym handling.
Link retrieval near zero for most submitted models shows that current fine-tuning alone does not teach models to locate regulatory documents, making retrieval augmentation the natural next step.
The certificate-task results suggest that reasoning-enhanced fine-tuning, as in FinMind-Y-Me, is a promising path toward professional exam performance.
Domain fine-tuning can trade away capabilities, since Uniandes beat its base Llama 3.1-8B on XBRL terms but fell below it on financial math and tag queries.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the 10%/20%/10% weight scheme is not defended against alternatives, and with abbreviation recognition sampled from thousands of stock tickers, a single hard subtask may drag scores down disproportionately.
Editorial inference: the poor link-retrieval results may partly reflect an exact-match scoring rule, so a model that returns the correct document in a different URL form gets no credit and the capability gap may be smaller or larger than the table shows.
Editorial inference: because certificate questions are private and withheld, the benchmark can be reused over time to detect pretraining contamination, since future models that improve on those questions without being trained on them would point to reasoning rather than memorization.
Editorial inference: merging the question sets into a public leaderboard would let the community test whether the reported weaknesses persist across newer model generations.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a straightforward report on the COLING 2025 Regulations Challenge shared task. It defines nine tasks across basic NLP, certificate exams, CDM, MOF licensing, and XBRL analytics, and evaluates six submitted systems plus three baselines. The headline result is that FinMind-Y-Me tops the submitted teams at 0.54801, still below GPT-4o's 0.63567. The paper is useful as a first snapshot, but the ranking's credibility hinges on a private, unvalidated certificate-question answer key (Task 6, 20% weight). That is the soft spot to worry about before citing the leaderboard.

What is genuinely new: the specific nine-task combination and the evaluation results on those tasks for these models. The paper also does a few things well methodologically: it discloses the time-constrained sampling, the dropped financial-ratio subtask, and the decision to keep the certificate set private. That transparency is real and appreciated. The task definitions are mostly clear, and the discussion of model weaknesses (abbreviation recognition, link retrieval) matches general findings.

Soft spots, in order of severity:

1. Task 6 is the load-bearing risk. The answer key is private, and the paper does not describe how it was validated against official exam keys, how disputed questions were resolved, or how the set was sampled. The CPA REG spread is wide (submitted teams 0.31–0.37, GPT-4o 0.71), so any systematic key error would move the ranking. The same validation gap applies to the LLM-generated QA sets scored with FActScore and BERTScore; the reference answers may not have been human-checked.

2. No data or code are released, so the benchmark cannot be reproduced. The paper says results will merge into the Open FinLLM Leaderboard, but nothing is available now.

3. The evaluation is underpowered: no significance tests, hand-chosen weights with no sensitivity analysis, and no contamination check. The random subsets (200/3298 stock tickers, 90/1000 financial math) are small and could add noise.

4. The novelty claim is a bit strong: most tasks are standard NLP tasks adapted to regulatory text. The contribution is curation and evaluation, not new task formulations.

The citation pattern is acceptable; the self-citations point to prior work the tasks build on, and the authors are open about that.

Overall, this is a decent workshop report that gives a first look at LLM performance on financial regulation. It is not a rigorous benchmark yet. A serious reviewer would want the certificate key released or at least validated, a sensitivity analysis for the weights, and basic significance testing. I would accept it for peer review with major revision because the problem is relevant and the transparency is above average for a shared-task report, but I would not cite it as a primary benchmark until the data and answer validation are public.

Referee Report

4 major / 6 minor

Summary. The paper reports on the Regulations Challenge, a shared task at COLING 2025 FinNLP-FNP-LLMFinLegal, in which the authors created nine tasks and corresponding question sets to evaluate LLMs on financial regulation understanding. The tasks cover abbreviation and definition recognition, NER, QA, link retrieval, CFA/CPA certificate-style questions, CDM, MOF licensing, and XBRL analytics. Six teams submitted full solutions; the paper describes their methods, reports per-task scores, and gives a final ranking obtained as a weighted average with 10% weight on Tasks 1–5 and 7–9 and 20% on Task 6. The top submitted system, FinMind-Y-Me, achieves 0.54801, below the GPT-4o baseline at 0.63567 and the Mistral Large 2 baseline at 0.62489. The paper claims that these nine tasks provide a meaningful evaluation of FinLLM professional capability in financial regulations, and it identifies strong QA performance and weak abbreviation and link-retrieval performance as notable findings.

Significance. If the measurement claims are valid, the paper provides one of the first structured, multi-area evaluations of LLMs on financial regulation compliance, with a useful decomposition across information retrieval, certificate-style reasoning, machine-readable standards (CDM, XBRL), and open-model licensing. The transparent disclosure of dataset sizes, metrics, sampling decisions, and the private status of the certificate set is a strength, as is the inclusion of three baselines and the later integration of results into the Open FinLLM Leaderboard. The main contribution is organizational and empirical rather than methodological: it produces a benchmark and a leaderboard that could be reused by future shared tasks. The reliability of the leaderboard, however, rests on question-set quality and answer-key correctness, which the paper does not currently demonstrate. Because several reported rank gaps are small, the absence of uncertainty quantification and the unvalidated private certificate task are material threats to the paper's central claim.

major comments (4)

[2.2 and Table 3] The certificate task (Task 6) is the least supported link in the evaluation chain. Section 2.2 states that the set contains 346 questions from CFA real/mock exams and CPA REG mock exams, is stored privately, and will not be released; however, it gives no description of how the authors verified the answer key against official exam keys, how ambiguous or disputed questions were resolved, or how the set was sampled. Task 6 carries 20% of the final score (Section 4.1), double the weight of each other task. Table 3 shows large cross-model spreads, e.g., REG CPA scores range from 0.2376 (finma) to 0.7129 (GPT-4o); any systematic error in the private answer key would therefore directly move the final ranking, including the margin between the top submitted system (0.54801) and the second-place system (0.43929). The authors should either provide an independent validation procedure for the answer key, report error-analysis statistics on disputed questions, or include a sensitivity analysis that re-ranks participants with Task 6 removed or reweighted.
[4.1 and Tables 3–4] The sampling and exclusion decisions in Section 4.1 are not accompanied by uncertainty estimates. The authors randomly sampled 200 of 3,298 stock-ticker questions and 90 of 1,000 financial-math questions, and they excluded the 50 financial-ratio-formula questions entirely. No confidence intervals, bootstrap estimates, or significance tests are reported for any score in Tables 3–4. This is consequential because several ranking differences are small: Uniandes (0.43929) and GGBond (0.43798) differ by 0.00131 in final score, and FinMind-Y-Me's lead over Uniandes is about 0.109, which is comparable to the 10% weight of a single task. The authors should report standard errors or bootstrap intervals for the final scores and for the affected subtask scores, and should state whether the ranking is stable across alternative random subsets.
[4.1] The hand-chosen weighting scheme is presented without justification or robustness analysis. Section 4.1 assigns 10% to each of Tasks 1–5, 20% to Task 6, and 10% to each of Tasks 7–9, but the paper does not explain why Task 6 deserves twice the weight of the others, nor does it test alternative weights. Given the small margins in the reported ranking, the top position among submitted teams could plausibly change under a different defensible weighting. The authors should provide a sensitivity analysis over reasonable weight vectors, or explicitly frame the ranking as contingent on this particular weighting and report the rank robustness.
[2.3–2.5 and Table 2] For the generated question sets (CDM, MOF, XBRL), the evaluation depends on gold or reference answers via FActScore and BERTScore, but the paper does not describe any human validation, inter-annotator agreement, or correction protocol for these answers. Section 2.3 indicates that 80 CDM QA pairs were generated from documentation and 46 were collected from FAQs and experts, while Section 2.5 relies on the XBRL Agent dataset; nowhere is there a statement that an independent annotator checked the reference answers for correctness. If the reference answers were produced by an LLM and used without verification, the reported scores may measure agreement with the generator rather than factual correctness. The authors should report the curation and validation procedure, including the number of human annotators and the agreement rate, or provide a sample of verified reference answers.

minor comments (6)

[4.3] In the discussion of certificate-task performance, the paper writes 'IntelliChain Starts'; this should be 'IntelliChain Stars' to match the team name used elsewhere.
[References] The reference for Chantangphol et al. contains 'based on thalle', which appears to be a typo; the intended model name should be spelled out correctly.
[Table 3 caption] The caption says 'The rankings of teams and evaluation results', but the table also lists baselines; the caption should state that baselines are included for comparison and are not ranked teams.
[5] The conclusion states that question sets will be merged into the Open FinLLM Leaderboard, but Section 2.2 says the certificate set will not be released; the paper should clarify exactly which subsets will be released and under what license.
[References] The reference for 'Audit-FT (Huang et al.)' lacks a year and full bibliographic details, and several workshop papers in the proceedings are cited without page numbers or publication years.
[References] The Mistral AI reference is listed as 'Large enough' and is not informative; it should cite the model card or technical report with a version number and access date.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the reported leaderboard scores are measured outputs on constructed question sets, not quantities derived from the inputs that define them; self-citations supply data sources but are not load-bearing.

full rationale

This paper is a shared-task benchmark report rather than a derivation, so there is no target quantity being predicted from fitted inputs. The nine tasks and question sets are assembled from official regulatory sources, external exam materials (CFA, CPA REG), CDM documentation, the MOF paper, and the authors' prior XBRL Agent dataset; the reported rankings are weighted averages of measured accuracy, F1, BertScore, and FActScore on those sets. No score is defined in terms of a claim it is supposed to predict, and no parameter is fitted and then renamed as a result. Self-citations to XBRL Agent, MOF, and the Open FinLLM Leaderboard provide data or framing, but the central ranking claims rest on observable model outputs rather than on those citations being true. The privately held and unreleased certificate question set is a transparency and validation limitation, not a circular step, because the certificate scores are externally sourced exam questions measured against an answer key rather than a quantity defined by this paper. Therefore no circularity is found.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper is an evaluation report rather than a derivation, so most assumptions are about benchmark validity rather than mathematical axioms. The free parameters are evaluation design choices that directly affect the reported rankings.

free parameters (3)

Task weighting scheme = 10% Tasks 1-5, 20% Task 6, 10% Tasks 7-9
Hand-chosen weights determine the final ranking; no sensitivity analysis is reported, so the winner could change under alternative weights.
Random subset sizes for two subtasks = 200 stock ticker items; 90 financial math items
Sampled due to time constraints; the excluded financial-ratio-formula subtask also changes Task 9 composition, so reported scores may not represent full datasets.
Validation/test split = 10% validation, 90% test; seed not reported
Random split without a stated seed prevents exact reproduction and can affect scores on small datasets.

assumptions (5)

domain assumption Reference answers for generated question sets are correct and unambiguous.
CDM, MOF, XBRL, and generated QA answers are created by organizers or sourced from public documents, but no expert validation or inter-annotator agreement is reported (Section 2, Tables 2-4).
domain assumption Test questions have not been memorized by models during pretraining.
CFA/CPA questions and public regulatory texts may appear in training corpora; no contamination analysis is presented (Section 4).
domain assumption The chosen metrics measure professional capability in financial regulations.
Accuracy, F1, BERTScore, and FActScore are used without a human correlation study (Section 4.1).
ad hoc to paper The weighted average of task scores is a meaningful method for ranking teams.
The 10%/20%/10% weights are introduced for this challenge without empirical justification or robustness checks (Section 4.1).
ad hoc to paper Randomly sampled subsets are representative of the full datasets.
Sampling 200 stock ticker items and 90 financial math items, and dropping financial ratio formulas, assumes no bias is introduced (Section 4.1).

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Cite this review

Pith. "Pith review of A Report on Financial Regulations Challenge at COLING 2025." pith.science (2026). https://pith.science/paper/5JTGLRCE

@misc{pith2026241211159,
  author       = {Pith},
  title        = {Pith review of: A Report on Financial Regulations Challenge at COLING 2025},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5JTGLRCE}},
  note         = {Machine review of arXiv:2412.11159}
}

read the original abstract

Financial large language models (FinLLMs) have been applied to various tasks in business, finance, accounting, and auditing. Complex financial regulations and standards are critical to financial services, which LLMs must comply with. However, FinLLMs' performance in understanding and interpreting financial regulations has rarely been studied. Therefore, we organize the Regulations Challenge, a shared task at COLING 2025. It encourages the academic community to explore the strengths and limitations of popular LLMs. We create 9 novel tasks and corresponding question sets. In this paper, we provide an overview of these tasks and summarize participants' approaches and results. We aim to raise awareness of FinLLMs' professional capability in financial regulations.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Open FinLLM Leaderboard: Towards Financial AI Readiness
cs.CE 2025-01 conditional novelty 3.0 of 10

The paper presents an open, continuously updated FinLLM leaderboard that aggregates existing financial benchmarks and demos for comparing models.

Reference graph

Works this paper leans on

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ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...
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" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023. Qwen technical report. arXiv preprint arXiv:2309.16609

arXiv 2023

[4] [4]

Markus Brunnermeier, Andrew Crockett, Charles Goodhart, Avi Persaud, and Hyun Shin. 2009. The fundamental principles of financial regulation. International Center for Monetary and Banking Studies Centre for Economic Policy Research, Geneva London

work page 2009

[5] [5]

Santiago Martínez Carrión, Juan Manuel Castañeda, and Rubén Manrique. 2024. Uniandes at the regulations challenge task: A scalable framework for legal text understanding in regulatory and financial contexts. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing...

work page 2024

[6] [6]

Pantid Chantangphol, Pornchanan Balee, Kantapong Sucharitpongpan, Chanatip Saetia, and Tawunrat Chalothorn. 2024. Finmind-y-me at the regulations challenge task: Financial mind your meaning based on thalle. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing ...

work page 2024

[7] [7]

Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2024. Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems, 36

2024

[8] [8]

Shijie Han, Haoqiang Kang, Bo Jin, Xiao-Yang Liu, and Steve Yang. 2024. XBRL Agent : Leveraging large language models for financial report analysis. In ACM International Conference on AI in Finance

work page 2024