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REVIEW 4 major objections 6 minor 76 references

VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read VisFinEval, a 15,848-question Chinese multimodal benchmark spanning the full financial workflow, finds the best current AI system scores 76.3% zero-shot—above non-expert humans but more than 14 points below financial experts.

desk verdict A genuinely useful Chinese multimodal financial benchmark whose leaderboard and scenario taxonomy need more evidence – the dataset is the contribution, the evaluation claims are conditional. read the letter →

arxiv 2508.09641 v1 pith:VQG4IB3W submitted 2025-08-13 cs.CE

Zhaowei Liu , Xin Guo , Haotian Xia , Lingfeng Zeng , Fangqi Lou , Jinyi Niu , Mengping Li , Qi Qi
show 6 more authors
Jiahuan Li Wei Zhang Yinglong Wang Weige Cai Weining Shen Liwen Zhang
This is my paper
classification cs.CE
keywords multimodallargelanguagemodelsChinesefinancialbenchmarkscenario-drivenevaluationzero-shotMLLMriskcontrolK-linechartsdocumentunderstandingerroranalysis
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

VisFinEval claims to be the first large-scale Chinese benchmark that evaluates multimodal large language models across the entire financial workflow, from front-office data reading to back-office risk control and asset optimization. It consists of 15,848 annotated question-answer pairs built from eight image types, such as K-line charts, financial statements, relationship graphs, and official seals, organized into three scenario depths. In zero-shot testing of 21 models, the best system reaches 76.3% accuracy, which beats finance-naive humans (56.4%) but trails a financial expert (88.0%) by more than 14 percentage points. The benchmark also exposes six recurring failure modes, including hallucination, cross-modal misalignment, and business-process reasoning gaps. If the benchmark is representative, it gives the community a practical yardstick for closing the gap between general-purpose multimodal models and expert-level financial analysis.

What carries the argument

The central object is the benchmark itself, VisFinEval: a three-tier scenario taxonomy (front-office data perception, mid-office analysis and decision support, back-office risk control and optimization) instantiated as 15,848 multiple-choice, true/false, and open-ended QA pairs over eight financial image modalities, including K-line charts, statements, relationship graphs, and official seals. The scenario-depth hierarchy is the load-bearing device: it orders tasks by required reasoning complexity and by proximity to real financial workflows, so that a model's score trajectory across tiers reveals where its financial competence breaks down.

What would settle it

Re-annotate a random sample of roughly 300 VisFinEval questions with independent financial experts who did not help design the dataset, and measure pairwise agreement; if agreement falls well below the unanimous-expert standard the paper claims, the answer keys are not as unambiguous as assumed. A second check: regenerate a matched set of questions with a non-Qwen generator and compare Qwen-VL-max's accuracy on the original versus regenerated set—a large drop would indicate Qwen-specific leakage in the original questions.

Watch

Extended reading notes

Core claim

The paper's central claim is that current multimodal large language models have not yet reached expert-level financial understanding, and that the gap is measurable and structured. VisFinEval operationalizes 'holistic financial understanding' as performance on tasks drawn from the front-middle-back office lifecycle: financial knowledge and data analysis, financial analysis and decision support, and financial risk control and asset optimization, with increasing complexity. On this instrument, the strongest evaluated model, Qwen-VL-max, achieves 76.3% overall accuracy; non-expert humans score 56.4% and a financial expert scores 88.0%. The paper further claims that model performance degrades sh

Load-bearing premise

The QA pairs are correct and unambiguous because they passed a three-stage filter whose generator, classifier, and judge are all Qwen-family models; if Qwen-specific wording or answer conventions are embedded in the questions, Qwen models' scores could be inflated, and the paper reports no inter-annotator agreement data to rule that out.

Editorial extensions

If this is right

  • If VisFinEval is a valid instrument, the best current multimodal models are already useful for front-office financial data tasks but not yet deployable for back-office risk control and asset optimization, where the top model scores only about 59%.
  • The more-than-14-point gap between the best model and a financial expert sets a concrete target for the next generation of domain-tailored models.
  • The six error categories provide a taxonomy that model developers can attack directly, such as improving cross-modal alignment and reducing hallucination in seal and chart reading.
  • The benchmark's environmental perturbations (occlusion, redundant images, missing information, irrelevant information) add robustness testing that existing financial benchmarks lack.
  • Open-source models trail the best closed-source model by only about 3.8 points, suggesting that high performance on this suite does not require a proprietary API.

Reading between the lines

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

  • Because the same model family generated, filtered, classified, and judged the questions, VisFinEval may systematically favor Qwen-family reasoning styles; a cross-family replication of the data pipeline would clarify how much of the 76.3% accuracy is a Qwen-specific artifact.
  • The human-expert baseline rests on roughly 300 questions answered by a single finance PhD candidate; the 88.0% expert ceiling may be noisy, and a larger expert panel could shift the gap estimate.
  • The benchmark's scenario weights are chosen for coverage, not real-world frequency; the paper itself notes weights matter, so reweighting scenarios could change model rankings and better predict deployment value.
  • The error taxonomy could be turned into a training curriculum: targeted data for cross-modal alignment and business-process reasoning would likely move scores on the hardest tier faster than general-purpose instruction tuning.
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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

4 major / 6 minor

Summary. VisFinEval introduces a Chinese multimodal financial benchmark with 15,848 QA pairs over eight image modalities, organized into a three-tier front/mid/back-office scenario hierarchy with fifteen sub-scenarios. Data generation uses Qwen-VL-Plus-latest on financial documents, followed by automatic filtering, manual annotation by undergraduate finance students, and review by three senior financial experts. The paper evaluates 21 MLLMs in a zero-shot setting, using Qwen-max-latest as the answer judge, and reports that Qwen-VL-max reaches 76.3% overall accuracy, outperforming a non-expert human (56.4%) and trailing a financial expert (88.0%). An error analysis identifies six recurring failure modes. The authors claim VisFinEval is the first large-scale Chinese benchmark spanning the full financial business lifecycle and release data and code at a public GitHub repository.

Significance. If the construction pipeline and evaluation protocol are sound, VisFinEval would be a substantial resource: it is large for the Chinese financial multimodal domain, covers diverse visual modalities, includes realistic perturbations and multi-turn/counterfactual tasks, and provides a broad comparison of 21 models. The scenario hierarchy and error taxonomy are useful organizing principles. However, several load-bearing aspects are not yet established. The paper's own appendix reports only 0.61 similarity between the automatic filter/classifier and human evaluation, the judge is validated by an undocumented '>98%' manual review, the scenario-classification prompt in Table 15 does not match the published taxonomy, and the human expert comparison rests on a single participant. These issues directly affect the validity of the headline accuracy numbers and the 'full-lifecycle' claim. The contribution is therefore promising but conditional on substantial verification and transparency.

major comments (4)
  1. [Section 3.2, Appendix A.2, Figure 3] The three-stage quality pipeline is the backbone of the benchmark, but its validation is insufficient. Figure 3(a) reports a similarity of 0.61 between the Qwen-VL-Plus-latest filter and human evaluation and explicitly labels this as a 'non-ideal match', while Section 3.2 describes the agreement as 'relatively high'. No inter-annotator reliability (e.g., Cohen's kappa) is reported for the six undergraduate annotators, and no audit statistics or examples are provided for the expert review. Because the QA content is the benchmark itself, the central claim that the 15,848 pairs are 'rigorously annotated' and unambiguous is not yet supported. Please release the filter/classifier decisions, annotation disagreement data, and expert correction records, or provide an independent human re-annotation sample with agreement coefficients.
  2. [Appendix C, Table 15 (and Table 16)] Table 15 is presented as the prompt used to classify QA pairs into the seven front-office scenarios, but its scenario list is inconsistent with Section 3.3. It omits Financial Indicator Assessment and Stock Selection Strategies Backtesting, and it includes Financial Market Sentiment Analysis and Financial Scenario Analysis, which Section 3.3 assigns to the mid-office layer. Table 16 also uses a mid-office category list that does not match the published Financial Scenario Analysis / Industry Analysis and Inference / Investment Analysis / Financial Market Sentiment Analysis taxonomy. If these prompts were actually used, the scenario labels and therefore the counts in Table 4 and the per-scenario scores in Table 2 are not reliable. If they are typos, the appendix does not substantiate the claimed classification quality. Please reconcile the prompts with the taxonomy, re-verify a random samp
  3. [Section 5.2, Table 3] The human comparison is based on one undergraduate 'non-expert' and one PhD candidate 'financial expert'. No variance, participant sampling, or inter-annotator agreement is reported, and the PhD candidate is not described as having the decade of experience attributed to the experts in Section 3.2. The abstract's claim that the best model 'trailing financial experts by over 14 percentage points' is therefore a single-participant observation rather than a robust baseline. Additional expert and non-expert participants, with per-item standard errors or a confidence interval, are needed before this comparison can support the stated conclusion.
  4. [Section 4.2] The judge model Qwen-max-latest is said to have been validated by 'manual review of all the results', with accuracy exceeding 98%, but no protocol, sample size, error examples, or inter-annotator agreement is provided. Since the same model family is used for generation (Qwen-VL-Plus-latest), filtering (Qwen-VL-Plus-latest), classification (Qwen-max), and judging (Qwen-max-latest), there is a risk of distributional alignment with Qwen-family models: Qwen-specific phrasing or answer style could inflate Qwen-family scores and systematically under-score non-Qwen models. This is not definitional circularity, but it is a correctness risk for all reported scores. Please report the manual review protocol in detail and add an independent judge or human annotation on a stratified sample, with agreement statistics.
minor comments (6)
  1. [Section 5.1] Typo: 'acuarcy' should be 'accuracy'.
  2. [Appendix B.1, Tables 2 and 5] The text says Moonshot-V1-32k-vision-preview 'far outperformed other models in the FSR task with the accuracy of 98.0', but both tables show 98.0 for Step-1o-vision-32k and 68.3 for Moonshot. The text and tables must be reconciled.
  3. [Table 6 and surrounding text] The abbreviation FMASA is used in some places and FMSA in others; standardize to FMSA (Financial Market Sentiment Analysis).
  4. [Section 5.2 vs. Table 2] The overall average for Qwen-VL-max is 73.9 in Table 3 but 76.3 in Table 2. The paper says the computations differ (sampled 2% and averaged over scenario groups), but the discrepancy should be explicitly explained to prevent reader confusion.
  5. [Appendix A.3] Several perturbation examples are heavily compressed and nearly illegible in the PDF; please ensure the released dataset and any camera-ready figures contain non-degraded versions so reviewers and readers can verify the perturbation types.
  6. [Section 3.2] The provenance of images from research reports, annual reports, and exam materials is described as 'verified to be free from copyright restrictions', but no licenses or explicit source documentation are given. Please add provenance and license details for all image sources.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the benchmark's scores are measured, not derived from its construction, and the few self-citations are not load-bearing.

full rationale

VisFinEval is a benchmark-construction paper, not a predictive derivation. The QA pairs are generated from images (Section 3.2), then filtered, manually annotated by students, and reviewed by three experts (Appendix A.2); no parameter is fit and no model score is predicted from the generation process. The accuracy figures in Table 2 are measured against the fixed dataset, so the top placement of Qwen-VL-max is not definitionally forced. The use of Qwen-family models for generation/filtering/classification/judging (Qwen-VL-Plus-latest, Qwen-max, Qwen-max-latest) is a real external-validity/contamination risk, but it is a bias hypothesis, not a logical circularity: the paper never equates the generator with the evaluator, and the judge was claimed to be manually checked. The self-citation to FinEval (Guo et al., 2024) is a peripheral pointer about model-size trends, not a load-bearing premise. The paper also contains data-quality flags that should be weighed separately from circularity: Appendix A.2/Figure 3 reports a 0.61 'non-ideal match' between the Qwen filter/classifier and human judgments, and the claimed 'manual review ... exceeded 98%' for the judge is not documented; Table 15's front-office classifier prompt lists seven scenarios that omit Financial Indicator Assessment and Stock Selection Strategies Backtesting while including Financial Market Sentiment Analysis and Financial Scenario Analysis, conflicting with Section 3.3. These issues undermine confidence in the scenario labels and rankings but do not make any result equivalent to its inputs by construction. Therefore no circular step is identified; the score of 2 reflects only the minor non-load-bearing self-citation and adjacent validity concerns.

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

There are no fitted constants or invented entities in this benchmark paper. The central assumptions are about data quality, judge reliability, human baseline validity, and contamination risk. The Qwen-family generation and judging pipeline is the main source of unexamined bias.

assumptions (5)
  • domain assumption The three-stage review guarantees correct unique answers for all QA pairs.
    Section 3.2 and A.2 describe automated filtering, student annotation, and expert review; no inter-annotator agreement or post-release audit is reported.
  • domain assumption Publicly sourced images are copyright-free and suitable for distribution.
    Section 3.2 asserts verification of copyright status, but gives no licenses or detailed provenance.
  • domain assumption Qwen-max-latest is a valid judge for extracting answers from model outputs.
    Section 4.2 claims manual verification shows over 98% judge accuracy, but the protocol, sample size, and raw agreement data are not provided.
  • domain assumption One undergraduate and one finance PhD candidate represent non-expert and expert human performance.
    Section 5.2 uses roughly 300 questions and one person per group, with no variance or confidence intervals.
  • domain assumption Zero-shot results are not inflated by training-data contamination.
    Images come from public reports, annual reports, and exam materials that could appear in pretraining corpora; the paper reports no contamination analysis.

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Cite this review

Pith. "Pith review of VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding." pith.science (2026). https://pith.science/paper/VQG4IB3W

@misc{pith2026250809641,
  author       = {Pith},
  title        = {Pith review of: VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VQG4IB3W}},
  note         = {Machine review of arXiv:2508.09641}
}
read the original abstract

Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the first large-scale Chinese benchmark that spans the full front-middle-back office lifecycle of financial tasks. VisFinEval comprises 15,848 annotated question-answer pairs drawn from eight common financial image modalities (e.g., K-line charts, financial statements, official seals), organized into three hierarchical scenario depths: Financial Knowledge & Data Analysis, Financial Analysis & Decision Support, and Financial Risk Control & Asset Optimization. We evaluate 21 state-of-the-art MLLMs in a zero-shot setting. The top model, Qwen-VL-max, achieves an overall accuracy of 76.3%, outperforming non-expert humans but trailing financial experts by over 14 percentage points. Our error analysis uncovers six recurring failure modes-including cross-modal misalignment, hallucinations, and lapses in business-process reasoning-that highlight critical avenues for future research. VisFinEval aims to accelerate the development of robust, domain-tailored MLLMs capable of seamlessly integrating textual and visual financial information. The data and the code are available at https://github.com/SUFE-AIFLM-Lab/VisFinEval.

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Works this paper leans on

76 extracted references · 74 canonical work pages

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    每道题的答案必须是 A、B、C 或 D 中的一个。

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    Each question should be no fewer than 10 Chinese characters in length

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    id": "1",

    **只输出 JSON 格式的内容，不要包含任何额外的描述性文本。** 你可以参考的示例: {random_few_shots} 输出格式为： [ {"id": "1", "q": " 问题1", "A": "选项A", "B": "选项B", "C": "选项C", "Answer": "正确答案"}, {"id": "2", "q": " 问题2", "A": "选项A", "B": "选项B", "C": "选项C", "Answer": "正确答案"}, {"id": "3", "q": " 问题3", "A": "选项A", "B": "选项B", "C": "选项C", "Answer": "正确答案"} ] (a) Chinese Version You are a financial ana...

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    金融情景分析：该场景包含假设性问题和反事实推理，例如“如果2022年 债券发行没有增加，将可能发生什么？”此类问题需要基于对金融机制的理解 推测可能后果。 请仅回答类别名称，不要解释。 47 (continued) Prompt Template for Classifying Scenarios in the Financial Knowledge and Data Analysis Category (English Version) Please classify the question based on the following content: {combined_text} Assign the question to one of the following seven financia...

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    Each question must be clearly stated, and the options should be meaningfully differentiated

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    The answer to each question must be one of A, B, C, or D

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    All three questions must be entirely different, and each should be labeled as 1, 2, and 3

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    id": "1",

    **Only output the content in JSON format. Do not include any additional descriptive text.** You may refer to the following examples: {random_few_shots} Output format: [ {"id": "1", "q": "Question 1", "A": "Option A", "B": "Option B", "C": "Option C", "Answer": "Correct Answer"...

  3. [14]

    每道问题必须使用中文语言，清晰明确，选项应具有区分度。

  4. [15]

    每道题的选项 A、B、C、D 应涵盖不同的可能性，避免过于简单或明 显。

  5. [16]

    每道题的答案必须是 A、B、C 或 D 中的一个，不能是其他内容。

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    三道题目必须完全不同，且每道题需要编号为 1、2、3。

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    id": "1",

    **只输出 JSON 格式的内容，不要包含任何额外的描述性文本。** 你可以参考的示例: {random_few_shots} 输出格式为： [ {"id": "1", "q": " 问题1", "A": "选项A", "B": "选项B", "C": "选项C", "D": "选 项D", "Answer": "A/B/C/D"}, {"id": "2", "q": " 问题1", "A": "选项A", "B": "选项B", "C": "选项C", "D": "选 项D", "Answer": "A/B/C/D"}, {"id": "3", ...

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    Each question must be written in Chinese, clearly stated, and the options should be distinguishable

  9. [20]

    Avoid options that are too simple or obvious

    Each question should have four options A, B, C, and D representing different possibilities. Avoid options that are too simple or obvious

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    必须基于所有图片内容，即每个题目都必须用到图对的所有图片的内 容，每个题目前面可以用一两句话描述下图片和题目，最后再次重申：使用 到所有图片的内容，题目尽量长一点。 你可以参考的示例: {random_few_shots} 输出格式（每个问题一个 JSON 对象）： [ { "q": "问题描述", "A": "选项A", "B": "选项B", "C": "选项C", "D": "选项D", "Answer": "正确答案"}, }, { "q": "问题描述", "A": "选项A", "B": "选项B", "C": "选项C", "D": ...

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    **只输出 JSON 格式的内容，不要包含任何额外的描述性文本。** 你可以参考的示例: {random_few_shots} 输出格式为： [ {{ "Question1":"","Answer":"True" }}, {{ "Question2":"","Answer":"True" }}, {{ "Question3":"","Answer":"True" }} ] (a) Chinese Version As a professional financial analyst with expertise in interpreting pi...

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    Question1

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    The trends should exhibit diversity (e.g., increase, decrease, fluctuation)

    **Data Diversity**: The line chart should display at least two distinct lines, each representing an independent data category or metric. The trends should exhibit diversity (e.g., increase, decrease, fluctuation)

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    The questions should involve some degree of calculation or reasoning

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    role": "user

    **Exclude Low-Quality Charts**: If the chart contains only one line, or if the data trend is overly simplistic (e.g., strictly increasing or decreasing), it should be excluded. ”’ messages=[{"role": "user", "content": [{"type": "image_url", "image_url": {"url": f"data:image/jp...

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    问题客观程度，需是客观题或者计算题 4.选项合理性（如为选择题） 验证结论格式：通过/不通过|理由 (a) Chinese Version Please validate the quality of the following QA pair: [Question] {question.get(’query’, ”)} [Reference Answer] {question.get(’answer’, ”)} Validation Criteria:

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    Question complexity (requires more than two steps of reasoning)

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    Objectivity of the question (must be objective or computational)

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    Option quality (if multiple-choice) Validation Output Format: Pass / Fail | Reason (b) English Version Table 15: Prompt Template for Classifying Scenarios in the Financial Knowledge and Data Analysis Category (Chinese Version) 请根据以下内容为问题进行分类：{combined_text} 请将问题分类到以下7个金融场景之一： ...

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    股 票K线 解 读 ： 该 场景通 过 解 读股 票K线 图及 相 关 技 术指 标 （如MACD、RSI、成交量等），分析股价的历史 走势、当前状态及未来 趋势。 3.智能验印：该场景需要对金融或行政文档中的印章进行识别、验证与比 对，以判断其真伪、归属及合规性。

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    金融信息识别：该场景关注金融、经济、投资领域中金融信息的识别和 解读任务，识别其所表达的金融含义。 财务数据统计：该场景关注对具体财务或经济数据的整理、趋势分析和对比 评估，例如地方政府债券发行量、资本项目差额、财政收支变动等。

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    财政扩张如何影响居民消费

    金融实体关系解读：该场景聚焦于经济主体（如公司、政府、部门）之 间的关系分析和经济影响链条解读，例如“财政扩张如何影响居民消费”或“资 本流入对汇率的影响”。

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    金融市场情绪洞察：该场景侧重从投资者行为、舆情或市场表现中提取 市场情绪趋势，例如通过新闻、评论、价格行为等数据推测市场预期。

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    Candlestick Chart Analysis, Intelligent Seal Recognition, Financial Information Extraction, Statistical Analysis of Financial Data, Interpretation of Financial Entity Relationships, Financial Market Sentiment Analysis, Financial Scenario Analysis Descriptions:

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    Candlestick Chart Analysis: This scenario involves interpreting candlestick charts and related technical indicators (e.g., MACD, RSI, trading volume) to analyze historical price trends, current states, and potential future movements

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    Intelligent Seal Recognition: This scenario requires identifying, verifying, and matching seals in financial or administrative documents to determine their authenticity, origin, and compliance

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    Financial Information Extraction: This scenario focuses on identifying and interpreting financial concepts and information in the fields of finance, economics, and investment. Statistical Analysis of Financial Data: This scenario focuses on organizing, analyzing trends, and co...

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    How does fiscal expansion affect house- hold consumption?

    Interpretation of Financial Entity Relationships: This scenario centers on analyz- ing the relationships among economic entities (e.g., firms, governments, departments) and tracing economic impact chains, such as "How does fiscal expansion affect house- hold consumption?" or "...

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    Financial Market Sentiment Analysis: This scenario emphasizes extracting market sentiment from investor behavior, public opinion, or market movements—e.g., inferring expectations through news, comments, or price behavior

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    What would have happened if bond issuance had not increased in 2022?

    Financial Scenario Analysis: This scenario involves hypothetical and counterfac- tual reasoning, such as "What would have happened if bond issuance had not increased in 2022?" These tasks require understanding financial mechanisms to infer potential outcomes. Please return onl...

    2022

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    产业分析推断 - 行业趋势、政策影响类问题

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    财务指标分析 - 涉及财务比率、指标计算

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    金融报表分析 - 资产负债表/利润表等解读

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    投资分析 - 综合投资决策评估 判断要求： 1.单轮问题直接根据问题内容分类

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    多轮问题需综合分析各轮次的关联性 3.背景信息可帮助理解问题场景 4.只需返回最匹配的中文类别名称 (a) Chinese Version [Financial Question Scenario Classification Task] Based on the following information, select the most appropriate category (return only the category name): [Background Context] {row[’background’]} {problem_p...

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    Industry Analysis and Inference – questions related to industry trends or policy impacts

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    Financial Performance Indicator Analysis – questions involving financial ratios or indicator calculations

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    Financial Statement Analysis – interpretation of balance sheets, income state- ments, etc

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    Investment Analysis – comprehensive evaluation of investment decisions Classification Guidelines:

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    For single-turn questions, classify based on the question content alone

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    For multi-turn questions, consider the relationship between all turns

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    Background context may assist in understanding the question

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    Only return the best-matching category name in Chinese (b) English Version 49 Table 17: Prompt Template for Classifying Scenarios in the Financial Risk Control and Asset Optimization Category (Chinese Version) 你现在是一位在金融领域的从业专家，请判断下列问题： 题目 {question} {options_text} 属于哪个【金融业务场景】...

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    Asset Allocation Analysis — related to portfolio structure, asset proportion adjustment, risk-return balancing, equity structure design, etc

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    Financial Strategy Optimization — focuses on corporate financial strategy adjustments (e.g., pricing/cost/marketing strategies) and their impact on profitability

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    Financial Data Reasoning and Interpretation — relies on numerical computation, financial indicator forecasting, inference of logical relationships between data, etc

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    Supplementary Note: The above descriptions are simplified and do not fully capture all cases under each scenario

    Financial Risk and Policy Analysis — includes identification of risks from exchange rate/interest rate fluctuations, analysis of policy impacts on financial markets (e.g., stock/bond markets) or firms, judgment of market risk signals (e.g., significant asset price volatility),...

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

    Mibench: A free, commercially representative embedded benchmark suite. In Proceedings of the fourth annual IEEE international workshop on work- load characterization. WWC-4 (Cat. No. 01EX538) , pages 3–14. IEEE. Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong ...

    2024 arXiv

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    arXiv preprint arXiv:2109.00122

    Finqa: A dataset of numerical reasoning over financial data. arXiv preprint arXiv:2109.00122. Zhiyu Chen, Shiyang Li, Charese Smiley, Zhiqiang Ma, Sameena Shah, and William Yang Wang. 2022. Con- vfinqa: Exploring the chain of numerical reasoning in conversational finance quest...

    2022 arXiv

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.