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arxiv: 2605.13330 · v1 · submitted 2026-05-13 · 💻 cs.CL

Recognition: no theorem link

FIND: Toward Multimodal Financial Reasoning and Question Answering for Indic Languages

Sarmistha Das , Vaibhav Vishal , Syed Ibrahim Ahmad , Manish Gupta , Sriparna Saha
Authors on Pith no claims yet

Pith reviewed 2026-05-14 20:21 UTC · model grok-4.3

classification 💻 cs.CL
keywords financial reasoningmultimodal question answeringIndic languagesbenchmark datasetnumerical reasoningmultilingual AIconstraint decoding
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0 comments X

The pith

FinVQA benchmark and FIND framework support accurate financial reasoning across six Indic languages and multiple modalities.

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

Financial decisions require combining numbers from tables and text, yet tools for this task remain weak in languages such as Hindi, Bengali, and Tamil. The paper introduces FinVQA, a collection of 18,900 questions that cover 14 financial domains, three difficulty levels, and four formats including multiple choice and table matching. It pairs the benchmark with FIND, a training and decoding approach that fine-tunes models while adding constraints to keep numerical answers faithful to the input. A sympathetic reader would expect this combination to produce systems that can handle real financial queries without inventing numbers or ignoring language-specific details. The work therefore targets a gap where errors carry direct monetary consequences.

Core claim

FinVQA spans English, Hindi, Bengali, Marathi, Gujarati, and Tamil with 18,900 samples across 14 financial domains, three difficulty levels, and four question formats. FIND combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning, robust multimodal grounding, and structured decision-making.

What carries the argument

The FIND framework, which pairs supervised fine-tuning of models with constraint-aware decoding that restricts outputs to remain consistent with provided numbers and tables.

If this is right

  • Models can be tested for numerical fidelity on realistic financial questions that mix text, tables, and calculations.
  • Constraint-aware decoding can reduce invented numbers in answers across four question formats.
  • Evaluation now exists for financial reasoning in six languages rather than English alone.
  • Structured decision-making becomes measurable through the dataset's difficulty tiers and format variety.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same fine-tuning plus decoding pattern could be applied to other high-stakes domains such as legal contract review in Indic languages.
  • Real-world financial chat tools might adopt FIND-style constraints to lower error rates when users ask in Hindi or Marathi.
  • Scaling the benchmark to additional Indic languages would test whether the current six-language coverage generalizes.

Load-bearing premise

The assumption that the FinVQA dataset captures diverse reasoning paradigms under realistic constraints across 14 financial domains, three difficulty levels, and four question formats in Indic languages.

What would settle it

If models using FIND still produce numerical contradictions on hard table-matching items in Tamil or Gujarati at rates no lower than standard fine-tuning, the benefit of constraint-aware decoding would be refuted.

Figures

Figures reproduced from arXiv: 2605.13330 by Manish Gupta, Sarmistha Das, Sriparna Saha, Syed Ibrahim Ahmad, Vaibhav Vishal.

Figure 1
Figure 1. Figure 1: FinVQA Corpus Overview. Illustrative samples spanning four distinct question paradigms: multiple [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Domain-wise distribution of FinVQA [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Difficulty-wise distribution of FinVQA. complement this foundation, we additionally incorporate professionally oriented content from the ICMAI–CMA program, administered by the Institute of Cost Accountants of India (ICMAI), 4NCERT website [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of the FinVQA dataset construction pipeline and the proposed FIND framework. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Inference results of the SFT models [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: OCR-Based Text Conversion [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of model performance across inference and training strategies. Rows correspond to different [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of model performance across inference and training strategies. Rows correspond to different [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
read the original abstract

Financial decision-making in multilingual settings demands accurate numerical reasoning grounded in diverse modalities, yet existing benchmarks largely overlook this high-stakes, real-world challenge, especially for Indic languages. We introduce FinVQA, a benchmark for evaluating financial numerical and multimodal reasoning in multilingual Indic contexts. FinVQA spans English, Hindi, Bengali, Marathi, Gujarati, and Tamil, and comprises 18,900 samples across 14 financial domains. The dataset captures diverse reasoning paradigms under realistic constraints, and is structured across three difficulty levels (easy, moderate, hard) and four question formats: multiple choice, fill-in-the-blank, table matching, and true/false. To address these challenges, we propose FIND, a framework that combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning, robust multimodal grounding, and structured decision-making. Together, FinVQA and FIND establish a rigorous evaluation and modeling paradigm for high-stakes multilingual multimodal financial reasoning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces FinVQA, a benchmark comprising 18,900 samples for financial numerical and multimodal reasoning across six languages (English, Hindi, Bengali, Marathi, Gujarati, Tamil), 14 financial domains, three difficulty levels, and four question formats. It also proposes the FIND framework, which combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning and robust multimodal grounding. The central claim is that FinVQA and FIND together establish a rigorous evaluation and modeling paradigm for high-stakes multilingual multimodal financial reasoning.

Significance. If the dataset construction and framework are properly validated with quantitative evidence, this work could fill a notable gap in multilingual financial QA benchmarks for Indic languages and support development of more grounded models in high-stakes domains. The scale and multilingual coverage are strengths, but the absence of any reported experiments, baselines, or validation metrics means the significance remains prospective rather than demonstrated.

major comments (3)
  1. [§3] §3 (FinVQA Construction and Validation): The manuscript details the 14 domains, three difficulty levels, and four formats but supplies no inter-annotator agreement scores, source-document fidelity checks, or native-speaker validation rates for the Indic-language instances. This is load-bearing for the claim that the benchmark captures diverse realistic reasoning paradigms under authentic constraints rather than synthetic artifacts.
  2. [§4] §4 (FIND Framework): The description of constraint-aware decoding lacks concrete implementation details, pseudocode, or ablation studies showing how it enforces numerical faithfulness and multimodal grounding. Without these, it is difficult to assess whether the framework delivers the promised improvements over standard fine-tuning.
  3. [§5] §5 (Experiments and Evaluation): No baseline results, error analysis, or performance metrics on FinVQA are reported. This undermines the assertion that FinVQA and FIND establish a rigorous evaluation paradigm, as there is no evidence the framework improves over existing approaches on the new benchmark.
minor comments (2)
  1. [Abstract] The abstract and introduction use the term 'parameter-free' in describing aspects of FIND; clarify whether this refers to the decoding constraints or another component, and ensure consistent usage throughout.
  2. [Table 1] Table 1 (dataset statistics) should include a breakdown by language and difficulty level to allow readers to assess balance across the 18,900 samples.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the manuscript requires additional quantitative validation for FinVQA, concrete implementation details for FIND, and experimental results to substantiate our claims. We will incorporate these elements in the revised version.

read point-by-point responses

  1. Referee: [§3] §3 (FinVQA Construction and Validation): The manuscript details the 14 domains, three difficulty levels, and four formats but supplies no inter-annotator agreement scores, source-document fidelity checks, or native-speaker validation rates for the Indic-language instances. This is load-bearing for the claim that the benchmark captures diverse realistic reasoning paradigms under authentic constraints rather than synthetic artifacts.

    Authors: We agree that explicit reporting of inter-annotator agreement, fidelity checks, and native-speaker validation is necessary to support the benchmark's authenticity. The current manuscript omitted these quantitative details to prioritize the overall dataset description. In the revised version, we will expand §3 with a dedicated validation subsection that reports inter-annotator agreement scores (Fleiss' kappa) per language and difficulty level, source-document fidelity metrics, and native-speaker validation rates for all Indic instances. These additions will directly address the concern and strengthen the claim that FinVQA reflects realistic financial reasoning. revision: yes

  2. Referee: [§4] §4 (FIND Framework): The description of constraint-aware decoding lacks concrete implementation details, pseudocode, or ablation studies showing how it enforces numerical faithfulness and multimodal grounding. Without these, it is difficult to assess whether the framework delivers the promised improvements over standard fine-tuning.

    Authors: We concur that the FIND framework description would benefit from greater specificity. The initial submission provided a high-level overview of the supervised fine-tuning plus constraint-aware decoding approach. In the revised manuscript, we will augment §4 with pseudocode for the decoding procedure, explicit definitions of the numerical and multimodal constraints employed, and ablation studies that isolate the contribution of each constraint type relative to standard fine-tuning. This will enable readers to evaluate the framework's mechanisms and improvements. revision: yes

  3. Referee: [§5] §5 (Experiments and Evaluation): No baseline results, error analysis, or performance metrics on FinVQA are reported. This undermines the assertion that FinVQA and FIND establish a rigorous evaluation paradigm, as there is no evidence the framework improves over existing approaches on the new benchmark.

    Authors: We acknowledge that the absence of reported experiments in the submitted manuscript limits the immediate demonstration of FIND's advantages on FinVQA. The work was initially structured to introduce the benchmark and framework, with empirical evaluation reserved for subsequent development. To resolve this, the revised manuscript will include a new experimental section presenting baseline results (e.g., standard multimodal models versus FIND), performance metrics across difficulty levels and languages, and error analysis. These results will provide initial evidence supporting the proposed paradigm. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset creation and framework proposal are self-contained introductions.

full rationale

The paper introduces FinVQA as a new benchmark dataset spanning languages, domains, difficulties, and formats, plus the FIND framework combining fine-tuning and decoding. No equations, fitted parameters, predictions, or self-citations appear in the provided text. The central claims rest on the construction and description of these new resources rather than any derivation that reduces to its own inputs by definition or fit. This matches the expected non-circular outcome for resource-creation papers.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

Claims rest on the creation of a new dataset and framework; no free parameters are fitted in the abstract, but domain assumptions about model capabilities and data realism are invoked without independent evidence.

axioms (2)
  • domain assumption Existing benchmarks largely overlook multilingual financial numerical reasoning in Indic languages
    Stated directly in the abstract as the motivation for FinVQA.
  • domain assumption Supervised fine-tuning combined with constraint-aware decoding promotes faithful numerical reasoning and robust multimodal grounding
    Core premise of the proposed FIND framework.
invented entities (2)
  • FinVQA no independent evidence
    purpose: Benchmark for financial numerical and multimodal reasoning in multilingual Indic contexts
    Newly introduced dataset spanning 18,900 samples, 14 domains, and multiple languages and formats.
  • FIND no independent evidence
    purpose: Framework combining supervised fine-tuning with constraint-aware decoding for financial reasoning
    Newly proposed modeling approach.

pith-pipeline@v0.9.0 · 5475 in / 1413 out tokens · 61490 ms · 2026-05-14T20:21:15.314836+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

19 extracted references · 6 canonical work pages · 2 internal anchors

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    Semantic Faithfulness: Text annotations must accurately reflect the intended meaning of the input question or content, without introducing unsupported assumptions or hallucinated in- formation

  8. [8]

    Conceptual Correctness: Ensure that all domain-specific concepts (e.g., financial, eco- nomic, statistical) are used correctly and con- sistently, adhering to standard definitions and conventions

  9. [9]

    Clarity and Precision: Textual explanations should be concise, unambiguous, and log- ically structured, avoiding unnecessary ver- bosity while preserving essential reasoning steps

  10. [10]

    Reasoning Transparency: When explanations are required, intermediate reasoning must align coherently with the final answer, ensur- ing traceability between assumptions, compu- tations, and conclusions

  11. [11]

    Irrelevant or speculative details should be excluded

    Context Appropriateness: Incorporate only the contextual information necessary to inter- pret or solve the task. Irrelevant or speculative details should be excluded

  12. [12]

    Consistency and Formatting: Maintain a uni- form annotation style across all samples, in- cluding consistent terminology, notation, and response structure

  13. [13]

    All annotations should remain neutral and task-focused

    Bias and Neutrality Check: Avoid subjec- tive, cultural, or demographic bias in textual descriptions and reasoning. All annotations should remain neutral and task-focused. B.2 Image Annotation and Validation Rules The following rules govern the annotation and vali- dation of visual inputs to ensure alignment, quality, and reliability in multimodal settings:

  14. [14]

    Visual content should support, not contradict, the intended reasoning task

    Rule 1: Text–Image Semantic Alignment The image must provide visual evidence that is directly relevant to the associated text or question. Visual content should support, not contradict, the intended reasoning task

  15. [15]

    Im- ages that are ambiguous, misleading, or in- sufficient to support reasoning should be ex- cluded

    Rule 2: Visual Evidence Sufficiency Annotate only those images where the neces- sary information is visually discernible. Im- ages that are ambiguous, misleading, or in- sufficient to support reasoning should be ex- cluded

  16. [16]

    Rule 3: No Embedded or Overlay Text Images must not contain embedded text, la- bels, watermarks, or annotations, as these can introduce unintended shortcuts or textual bias

  17. [17]

    Low-resolution, blurred, or cluttered visuals that hinder interpretation are not permitted

    Rule 4: Visual Quality and Readability Images should be clear, properly cropped, and well-illuminated. Low-resolution, blurred, or cluttered visuals that hinder interpretation are not permitted

  18. [18]

    Rule 5: Numerical and Structural Integrity For charts, tables, or financial visuals, ensure that axes, symbols, and numerical values are visually intact and correctly rendered, without distortion or truncation

  19. [19]

    Images exhibiting anatomical inconsistencies or AI-induced ar- tifacts should be rejected

    Rule 6: Human and Object Realism Any depicted humans or objects must appear natural and undistorted. Images exhibiting anatomical inconsistencies or AI-induced ar- tifacts should be rejected. These guidelines ensure that textual and visual annotations remain semantically aligned, logically coherent, and visually reliable, thereby enabling ro- bust multimo...

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