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REVIEW 5 major objections 6 minor 1 cited by

BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation

T0 review · 5 major / 6 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read This paper tries to establish that a financial LLM benchmark built from authentic user questions on Chinese and U.S. equity markets, with both offline tasks and online trading simulation, measures real business capability better than simula

desk verdict A genuinely useful financial benchmark idea with real user data and an online trading simulation, but the accuracy scoring is under-specified and the expert baseline is partly self-referential, so the headline numbers are not yet trustworthy. read the letter →

arxiv 2601.06401 v2 pith:2ZU4CENA submitted 2026-01-10 cs.AI cs.CL

Xin Guo , Rongjunchen Zhang , Guilong Lu , Xuntao Guo , Shuai Jia , Zhi Yang , Liwen Zhang This is my paper · ORCID
classification cs.AIcs.CL
keywords financialbenchmarkLLMevaluationauthenticbusinessdatabilingualofflineandonlineequitymarketsquestionansweringportfolioassetallocation
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

The paper argues that LLM financial capability can only be trusted when measured against real user questions drawn from operating equity-market platforms, not simulated or generic data. It introduces BizFinBench.v2, a bilingual benchmark of 29,578 expert-validated Q&A pairs spanning eight offline tasks and two online tasks, and reports that the best proprietary model reaches 61.5% accuracy while financial experts reach 84.8%. It also claims that in an online portfolio-allocation simulation with realistic trading rules, a reasoning-optimized open-weight model outperforms all evaluated commercial models. If true, the benchmark would provide a more reliable basis for deciding when and where LLMs can be deployed in financial services, and would show that current models are not yet business-ready.

What carries the argument

The load-bearing object is the benchmark itself: a corpus clustered from real platform queries into four scenarios (Business Information Provenance, Financial Logic Reasoning, Stakeholder Feature Perception, Real-time Market Discernment) and ten tasks, filtered by a three-level review (platform clustering and desensitization, frontline staff screening, senior expert cross-validation). For online tasks, the key mechanism is a real-time investment simulation with realistic market rules and hourly decisions, plus interval-based verification for sentiment and price prediction using preset business tolerances (10% for sentiment, 1% for stock price). This machinery converts 'LLM financial ability'

What would settle it

Take a random sample of, say, 500 offline questions, have an independent panel of senior financial experts label them blind, and compare the original labels; if agreement falls below about 95% or a model's score shifts by more than a few points under the alternate labels, the 61.5%-vs-84.8% gap is not stable. Alternatively, re-running the online portfolio task over a different market period would show whether the ranking of investment efficacy holds or was specific to the original five-week window.

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Extended reading notes

Core claim

The central claim is that a benchmark grounded in authentic user queries from Chinese and U.S. equity markets, with both offline and online tracks, can reveal gaps that simulated benchmarks hide. In the paper's own results, the gap is large: the best closed model scores 61.5% against 84.8% for financial experts across the offline tasks, and every evaluated model struggles on subjective sentiment scoring and price prediction. The paper further claims that the online Portfolio Asset Allocation task, run under realistic trading rules with transaction fees, latency, and slippage, shows a reasoning-focused open-weight model generating a 13.46% total return with a -8% maximum drawdown, beating all

Load-bearing premise

The central numbers assume every one of the 29,578 Q&A pairs has a correct, unambiguous, expert-validated answer, even though the paper's quality-control section says the evaluation rubrics and quantitative standards cannot be disclosed and the dataset is not yet released; if the hidden labels are inconsistent or biased, the accuracy scores and the expert-vs-model gap are not reliable.

Editorial extensions

If this is right

  • Reported accuracy on simulated finance benchmarks overstates what models can do on real business queries; deployment decisions should use real-data evaluation.
  • The 23-point gap between the best model (61.5%) and financial experts (84.8%) sets a concrete threshold that financial LLMs must close before being considered business-ready.
  • Domain-specific finance models do not automatically beat stronger general models, so finance-specialized training needs to be evaluated on authentic tasks, not just knowledge tests.
  • Chain-of-thought prompting is not a reliable universal boost in finance: it lowers average accuracy for most models, though it helps some, so reasoning strategy must be chosen per model and task.
  • Online simulation under real trading rules gives an investment-performance metric (return, Sharpe, drawdown) that static QA accuracy cannot capture.

Reading between the lines

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

  • Editorial inference: since the dataset and rubrics are not yet released, the headline gap is provisional until an independent panel can re-label a sample.
  • Editorial inference: the online asset-allocation ranking covers a single five-week window (November 19 to December 24, 2025); investment efficacy may be regime-dependent, so the ranking should not be generalized across market conditions without longer-horizon runs.
  • Editorial inference: a testable extension the paper leaves implicit is to use the same real-user corpus to build an instruction-tuning set; if fine-tuning on authentic business data narrows the expert gap more than fine-tuning on simulated data, the authenticity argument is directly validated.
  • Editorial inference: the five named error categories could be turned into diagnostic probes for model development, allowing developers to target specific failure modes rather than optimize a single aggregate score.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper introduces BizFinBench.v2, a bilingual benchmark for financial LLM evaluation built from real user queries on Chinese and U.S. equity-market platforms. It contains 29,578 question-answer pairs across eight offline tasks (AIT, FMP, FDD, FQC, ELR, CI, SA, FRA) and two online tasks (stock price prediction and portfolio asset allocation). Twenty-one LLMs and two financial experts are evaluated. The paper reports that the best model, ChatGPT-5, achieves 61.5% average accuracy while financial experts reach 84.8%, and that DeepSeek-R1 gives the best online investment performance. A five-category error analysis is provided. The central claims are that the benchmark is authentic, large-scale, cross-market, and dual-mode, and that current LLMs remain far below expert-level financial capability.

Significance. If the benchmark and its scoring are sound, BizFinBench.v2 would be a valuable contribution: it addresses a real gap by using authentic business data, covers two major equity markets, incorporates online evaluation, and includes an expert comparison. The scale (29,578 pairs), the dual offline/online design, and the business-oriented error taxonomy are strengths that distinguish it from many existing finance benchmarks. However, the central quantitative claims are currently threatened by an underspecified accuracy metric for most tasks, a self-referential expert threshold, and the absence of released data and rubrics. The contribution is potentially significant but needs substantial methodological clarification before the headline results can be accepted.

major comments (5)
  1. [§4.1 and Table 2] Accuracy is defined only for SA and SPP, via the interval_boxed{} tolerance check (10% and 1%, respectively). For AIT, FMP, FDD, FQC, ELR, CI, and FRA, the paper calls the questions 'verifiable open-ended questions' but does not specify the output-matching rule: no exact-match criterion, parser, partial-credit scheme, or human-scoring protocol is described. Without this mapping, every accuracy value in Table 2—including the headline 61.5% vs 84.8% gap—is uninterpretable. This is especially serious for the expert comparison, because a format-sensitive automated matcher could unfairly penalize human responses, while a lenient human-matching rule could inflate expert scores. The authors must disclose the scoring procedure for each task or release the evaluation code.
  2. [§3.3 and §5 (Table 2)] The 84.8% 'practical business requirement' is not an independent threshold: it is the average accuracy of the same six experts who constructed and cross-validated the ground-truth labels. The quality-control rubrics used to judge both experts and models are explicitly withheld (§3.3), making the experts-vs-models comparison partly self-referential. Moreover, Table 2 shows expert performance is not uniformly superior: experts score 32.3% on SPP, below the best model's 36.9%, and 57.9% on SA. Please (a) report the distribution of expert judgments and inter-expert agreement, (b) define the 'business requirement' independently of the experts' own accuracy on the same labeled set, and (c) provide the rubrics or an independent audit of answer unambiguity.
  3. [Table 2 and §5] The difference between the top two models is 0.2 percentage points (ChatGPT-5 61.5% vs Gemini-3 61.3%), yet the text describes 61.5% as 'prominent' and assigns a definitive first-place ranking. No standard errors, confidence intervals, bootstrap estimates, or significance tests are reported. With per-task sample sizes around 2,000–4,000, a 0.2-point overall difference is within plausible sampling noise. The authors should report uncertainty (e.g., confidence intervals or pairwise significance tests with multiple-comparison correction), or temper the ranking and 'prominent' language.
  4. [§4.2 and References (Fin-R1)] The paper evaluates Fin-R1 (Liu et al., 2025b), a financial LLM developed by the same research group (Shanghai University of Finance and Economics; several Fin-R1 authors overlap with the present authors). This overlap is not disclosed in the manuscript. In a comparative benchmark, evaluating one's own model without acknowledging the affiliation risks biasing the interpretation of the 'financial LLM' comparison. Please add a conflict-of-interest disclosure and discuss whether Fin-R1 was treated identically to all other models.
  5. [Abstract vs §3.1 / §3.3] The abstract states 'data and code are available,' but §3.1 says the dataset 'will be released as open-source in the future,' and §3.3 says the evaluation rubrics 'cannot be disclosed.' For a benchmark paper, the evaluation resource and its scoring procedure are the central artifact; withholding both makes the reported numbers unfalsifiable. Please provide the data and scoring code at least to reviewers (e.g., supplementary material), or specify a concrete release date and an independent audit procedure for the withheld rubrics.
minor comments (6)
  1. [Abstract] The abstract reports 28,860 questions, while §3.4 and Table 5 report 29,578. These numbers must be reconciled.
  2. [§4.2 / References] ChatGPT-5 is cited to 'OpenAI, 2023' (the GPT-4 technical report). Please cite the appropriate model documentation or release note for the actually evaluated version (2025.11, per Table 6).
  3. [§3.4 and Table 5] The text gives the CI average input token count as 5,510, but Table 5 lists 2,267 for CI and uses 5,510 for SPP. The text/table inconsistency should be fixed.
  4. [General] Typographical errors: 'Propretary' in Table 2 heading and 'disconnetion' in §1; the SHAFER and VOVK author name appears as 'V ovk' in §4.1. A careful proofread is needed.
  5. [§5 / Table 4] The CoT results are reported without significance tests, and no expert reference is provided for the CoT condition. If CoT is meant to be a secondary analysis, please state so explicitly and add appropriate caveats.
  6. [Figure 1 / Figure 2] Figure 2 is referenced as the 'complete model ranking' but its bars are not legible in the provided rendering; please ensure vector graphics or high-resolution images are used in the final version.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: benchmark measurements are self-contained; expert baseline uses independent evaluators.

full rationale

The paper's central claims are measured accuracies and investment metrics, not quantities derived from fitted parameters or from the benchmark's own labels. The expert-level baseline is explicitly produced by two financial experts 'who were not involved in the data construction process' (§4.2), so the 84.8% figure is not a self-referential relabeling of the ground-truth creators' own judgments. Overlapping-author citations (FinEval, Fin-R1) appear only as related-work comparisons or as one of the 21 evaluated models; neither is load-bearing for the benchmark's validity, and the paper does not rely on a uniqueness theorem or ansatz from those works. The most serious issue is methodological rather than circular: §4.1 specifies an interval-based Accuracy rule only for SA and SPP and leaves the scoring of the other seven open-ended tasks undefined, with the underlying rubrics also withheld (§3.3). That omission prevents independent verification but does not make any reported result equivalent to its inputs by construction. No circular step can be exhibited from the paper's own equations or definitions.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

Central claims rest on the authenticity of undisclosed user data, the correctness of expert labels whose rubrics are withheld, and hand-set interval tolerances that directly determine task accuracy. No fitted model is used, so the free-parameter count is low, but the empirical validity rests on unverifiable assumptions.

free parameters (2)
  • SA tolerance = 10%
    Hand-set business tolerance for sentiment interval scoring in §4.1; a larger tolerance would inflate SA accuracy.
  • SPP tolerance = 1%
    Hand-set tolerance for stock-price prediction interval scoring in §4.1; directly controls SPP accuracy.
assumptions (4)
  • domain assumption The 29,578 Q&A pairs are authentic, non-synthetic user data from real financial platforms.
    §3.1/§3.3 assert authenticity but provide no raw data/provenance audit; this is the load-bearing premise for the benchmark's value.
  • domain assumption Expert labels are correct, unambiguous gold standards even though quantitative rubrics are withheld.
    §3.3 states the rubrics cannot be disclosed, so external verification of label quality is impossible; expert consensus is assumed.
  • domain assumption The PAA simulation (with fees, latency, slippage) and its single five-week window are representative of real investment efficacy.
    §3.2/Table 3 report one simulation period (2025-11-19 to 2025-12-24) without sensitivity or multiple-period analysis.
  • ad hoc to paper Interval scoring with 10% (SA) and 1% (SPP) tolerances is a valid way to measure sentiment analysis and price prediction capability.
    §4.1 introduces these tolerances by hand; the accuracy numbers depend directly on them.

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Cite this review

Pith. "Pith review of BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation." pith.science (2026). https://pith.science/paper/2ZU4CENA

@misc{pith2026260106401,
  author       = {Pith},
  title        = {Pith review of: BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2ZU4CENA}},
  note         = {Machine review of arXiv:2601.06401}
}
read the original abstract

Large language models are becoming increasingly significant in financial applications. Nevertheless, prevailing benchmarks are largely dependent on simulated or generic data, which leads to a significant gap between reported performance and actual efficacy in real-world scenarios. To tackle this challenge, we present BizFinBench.v2, the first integrated offline and online benchmark built upon authentic user query-response data from both Chinese and U.S. equity markets. It comprises 28,860 questions across eight offline and two online tasks. Experimental results show that GPT-5 achieves a mere 61.5% accuracy, still failing to meet the practical business requirement (84.8%). Among the evaluated commercial models, DeepSeek-R1 exhibits superior investment efficacy. Error analysis grounded in real financial practice reveals persistent limitations in existing models. By overcoming the constraints of prior benchmarks, BizFinBench.v2 provides a substantiated foundation for advancing LLM deployment in the financial sector. Our data and code are available at https://github.com/HiThink-Research/BizFinBench.v2.

Figures

Figures reproduced from arXiv: 2601.06401 by the authors.

Figure 1
Figure 1. BizFinBench.v2 comprises eight foundational [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. We have ranked the performance of the LLMs participating in the evaluation under the zero-shot setting, [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. The platform’s user structure is primarily [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: We selected representative LLMs for error [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: An Example of Abnormal Information Tracing. This task requires the LLM to identify relevant information [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Example of the Financial Multi-turn Perception task. This task requires the LLM to identify the [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Example of the Financial Data Description task. This task requires the LLM to evaluate the logic [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Example of the Financial Quantitative Computation task. This task requires the model to retrieve relevant [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Example of the Event Logical Reasoning task. This task requires the LLM to arrange various financial [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Example of the Counterfactual Inference task. This task requires the LLM to perform counterfactual [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]
Figure 11
Figure 11. Figure 11: Example of the User Sentiment Analysis task. This task requires the LLM to evaluate a user’s emotional [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]
Figure 12
Figure 12. Figure 12: Example of the Financial Report Analysis task. This task requires the LLM to rank multiple companies [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]
Figure 13
Figure 13. Figure 13: The Stock Price Prediction task primarily requires the LLM to perform online predictions of individual [PITH_FULL_IMAGE:figures/full_fig_p023_13.png]
Figure 14
Figure 14. Figure 14: Display of the Portfolio Asset Allocation task webpage. The Portfolio Asset Allocation task primarily [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]
Figure 15
Figure 15. Figure 15: Display of Investment Details for DeepSeek-R1 [PITH_FULL_IMAGE:figures/full_fig_p025_15.png]
Figure 16
Figure 16. Figure 16: System Prompt for Portfolio Asset Allocation [PITH_FULL_IMAGE:figures/full_fig_p026_16.png]
Figure 17
Figure 17. Figure 17: Example of Financial Semantic Deviation. On the one hand, the model erroneously forced a strong [PITH_FULL_IMAGE:figures/full_fig_p027_17.png]
Figure 18
Figure 18. Figure 18: Example of Long-term Business Logic Discontinuity. In its rating of Company C, the model deliv [PITH_FULL_IMAGE:figures/full_fig_p028_18.png]
Figure 19
Figure 19. Figure 19: Example of Multivariate Integrated Analysis Deviation. The model’s output yielded an overly pessimistic [PITH_FULL_IMAGE:figures/full_fig_p029_19.png]
Figure 20
Figure 20. Figure 20: Example of High-precision Computational Distortion. While the model correctly selected the compound [PITH_FULL_IMAGE:figures/full_fig_p030_20.png]
Figure 21
Figure 21. Figure 21: Example of Financial Time-Series Logical Disorder. The model misjudged the market environment event [PITH_FULL_IMAGE:figures/full_fig_p031_21.png]
Figure 22
Figure 22. Figure 22: Answers from Grok-4, DeepSeek-V3.2 and Qwen3-32B to the same question in CI tasks. As evidenced [PITH_FULL_IMAGE:figures/full_fig_p032_22.png]

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Forward citations

Cited by 1 Pith paper

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  1. FinResearchBench II: A Deep Research Benchmark with Consensus-Derived Gold Rubrics for Distinguishing Financial Report Quality

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    A multi-LLM consensus pipeline turns 14,450 auto-generated candidate rubrics into 2,600 distinguishable gold rubrics that rank 10 financial deep-research systems from 58.58% to 22.23% pass rate.

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Works this paper leans on

2 extracted references · 2 linked inside Pith · cited by 1 Pith paper

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    Access" column shows whether we have full access to the model weights or we can access through API. The “Version Date

    Quantagent: Seeking holy grail in trading by self-improving large language model.arXiv preprint arXiv:2402.03755. Saizhuo Wang, Hang Yuan, Leon Zhou, Lionel Ni, He- ung Yeung Shum, and Jian Guo. 2025. Alpha-gpt: Human-ai interactive alpha mining for quantitative in- vestment. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Pr...

    arXiv 2025

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    arXiv preprint arXiv:2405.01769

    A survey on large language models for criti- cal societal domains: Finance, healthcare, and law. arXiv preprint arXiv:2405.01769. Josiel Delgadillo, Johnson Kinyua, and Charles Mutigwe. 2024. Finsosent: Advancing financial mar- ket sentiment analysis through pretrained large lan- guage models.Big Data and Cognitive Computing, 8(8):87. Team GLM, Aohan Zeng...

    arXiv 2024

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