arxiv: 2604.23588 · v1 · submitted 2026-04-26 · 💻 cs.AI · cs.CL· cs.IR

Recognition: unknown

FinGround: Detecting and Grounding Financial Hallucinations via Atomic Claim Verification

Dongxin Guo, Jikun Wu, Siu Ming Yiu

Authors on Pith no claims yet

Pith reviewed 2026-05-08 06:21 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.IR

keywords financial hallucination detectionatomic claim verificationRAG evaluationfinancial document QAhallucination mitigationretrieval-equalized evaluation

0 comments

The pith

FinGround decomposes financial answers into atomic claims verified by type-specific rules to cut hallucinations by 68 percent even with identical retrieval.

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

Financial AI systems frequently fabricate numbers, citations, and calculations from regulatory documents, risking regulatory penalties. The paper presents a three-stage pipeline that retrieves from text and tables, splits responses into atomic claims sorted by a six-type financial taxonomy, and applies routed verification including formula reconstruction against structured data. It introduces retrieval-equalized evaluation so that gains can be attributed to verification rather than better search. The approach yields large drops in unsupported claims relative to baselines and to GPT-4o while supporting an 8B distilled model that keeps 91 percent F1 at far lower latency.

Core claim

FinGround is a verify-then-ground pipeline whose core is atomic claim decomposition classified into a six-type financial taxonomy, followed by type-routed verification that includes formula reconstruction for computational claims, and final rewriting of unsupported claims with paragraph- and table-cell citations. Under retrieval-equalized conditions the full system reduces hallucination rates by 68 percent over the strongest baseline and by 78 percent relative to GPT-4o.

What carries the argument

Atomic claim decomposition classified by a six-type financial taxonomy, with verification routed by claim type and including arithmetic reconstruction against tables.

If this is right

Retrieval-equalized evaluation isolates the contribution of verification from retrieval quality.
Type-routed verification addresses the 43 percent of errors that require re-calculation against tables.
Grounding supplies explicit paragraph and table-cell citations for each supported claim.
An 8B distilled detector retains 91.4 percent F1 at 18 times lower latency than the full model.

Where Pith is reading between the lines

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

The same decomposition and routing pattern could be tested on legal or medical documents that also contain structured tables and formulas.
Widespread use of the distilled model would make grounded financial QA feasible at low marginal cost per query.
The taxonomy could be extended with additional claim types if new error patterns appear in broader financial datasets.

Load-bearing premise

Financial answers can be reliably split into atomic claims that match a six-type taxonomy and that type-specific checks will catch the computational errors that uniform detectors miss.

What would settle it

A test set of financial questions whose hallucinations consist mainly of arithmetic mistakes not captured by the taxonomy's formula-reconstruction step would show that the verification stage fails to reduce those errors.

Figures

Figures reproduced from arXiv: 2604.23588 by Dongxin Guo, Jikun Wu, Siu Ming Yiu.

**Figure 1.** Figure 1: The FINGROUND pipeline. Stage 1 classifies query complexity and retrieves hybrid text-and-table evidence. Stage 2 decomposes answers into atomic financial claims, aligns each to evidence, and classifies verdicts using a distilled 8B model. Stage 3 rewrites contradicted/unverifiable claims with cited evidence; supported claims pass through unchanged. swer. All systems’ outputs are decomposed using the same … view at source ↗

read the original abstract

Financial AI systems must produce answers grounded in specific regulatory filings, yet current LLMs fabricate metrics, invent citations, and miscalculate derived quantities. These errors carry direct regulatory consequences as the EU AI Act's high-risk enforcement deadline approaches (August 2026). Existing hallucination detectors treat all claims uniformly, missing 43% of computational errors that require arithmetic re-verification against structured tables. We present FinGround, a three-stage verify-then-ground pipeline for financial document QA. Stage 1 performs finance-aware hybrid retrieval over text and tables. Stage 2 decomposes answers into atomic claims classified by a six-type financial taxonomy and verified with type-routed strategies including formula reconstruction. Stage 3 rewrites unsupported claims with paragraph- and table-cell-level citations. To cleanly isolate verification value from retrieval quality, we propose retrieval-equalized evaluation as standard methodology for RAG verification research: when all systems receive identical retrieval, FinGround still reduces hallucination rates by 68% over the strongest baseline ($p < 0.01$). The full pipeline achieves a 78% reduction relative to GPT-4o. An 8B distilled detector retains 91.4% F1 at 18x lower per-claim latency, enabling $0.003/query deployment, supported by qualitative signals from a four-week analyst pilot.

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.

Desk Editor's Note private letter to a colleague

FinGround adds a finance-specific taxonomy and retrieval-equalized evaluation to claim verification, but its headline gains rest on unvalidated atomic decomposition.

read the letter

The main thing to know is that this paper builds a three-stage pipeline for financial QA that decomposes answers into atomic claims, routes them through a six-type taxonomy with formula reconstruction for arithmetic checks, and rewrites unsupported parts with citations. It also pushes retrieval-equalized evaluation so verification performance can be measured separately from retrieval quality. When all systems get the same retrieved context, the method claims a 68% drop in hallucinations over the best baseline and 78% versus GPT-4o, plus an 8B distilled version that keeps 91.4% F1 at much lower cost.

Referee Report

2 major / 1 minor

Summary. The paper introduces FinGround, a three-stage verify-then-ground pipeline for financial document QA. Stage 1 performs finance-aware hybrid retrieval over text and tables. Stage 2 decomposes answers into atomic claims classified by a six-type financial taxonomy and verifies them with type-routed strategies including formula reconstruction. Stage 3 rewrites unsupported claims with paragraph- and table-cell-level citations. The authors propose retrieval-equalized evaluation as a standard methodology and claim that, when all systems receive identical retrieval, FinGround reduces hallucination rates by 68% over the strongest baseline (p < 0.01), achieves a 78% reduction relative to GPT-4o, and that an 8B distilled detector retains 91.4% F1 at 18x lower per-claim latency.

Significance. If the empirical claims hold after validation, the work could meaningfully advance reliable financial AI systems given the EU AI Act timeline. The retrieval-equalized evaluation methodology is a clear strength that helps isolate verification gains from retrieval quality in RAG research. The efficiency of the distilled 8B detector and the mention of a real-world analyst pilot add practical relevance for deployment.

major comments (2)

Abstract and Stage 2: The 68% hallucination reduction under retrieval-equalized evaluation depends on reliable atomic claim decomposition and classification via the six-type financial taxonomy, plus type-routed verification catching the cited 43% of computational errors. No inter-annotator agreement, decomposition error rates, or taxonomy coverage statistics are reported, so the measured gains could be artifacts of claim splitting or labeling rather than genuine verification improvements.
Abstract: Performance numbers (68% and 78% reductions, 91.4% F1) are presented without data, error bars, implementation details, or description of how the taxonomy and verification rules were developed or validated, which directly undermines assessment of the central empirical claims.

minor comments (1)

Abstract: The reference to 'qualitative signals from a four-week analyst pilot' lacks any details on design, findings, or how it supports the quantitative claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for their detailed and insightful comments, which have helped us identify areas where the manuscript can be strengthened. We address each major comment below and commit to incorporating the suggested improvements in the revised version.

read point-by-point responses

Referee: Abstract and Stage 2: The 68% hallucination reduction under retrieval-equalized evaluation depends on reliable atomic claim decomposition and classification via the six-type financial taxonomy, plus type-routed verification catching the cited 43% of computational errors. No inter-annotator agreement, decomposition error rates, or taxonomy coverage statistics are reported, so the measured gains could be artifacts of claim splitting or labeling rather than genuine verification improvements.

Authors: We agree that additional validation metrics for the claim decomposition and taxonomy classification would enhance the credibility of our results. In the revised manuscript, we will add inter-annotator agreement scores based on a double-annotated subset of claims, along with decomposition error rates and taxonomy coverage statistics. The 43% computational error rate is derived from our error categorization in Section 4.3. We will also provide more details on the type-routed verification strategies to show how they specifically address these errors, ensuring the reported gains reflect genuine improvements in verification rather than artifacts of the decomposition process. revision: yes
Referee: Abstract: Performance numbers (68% and 78% reductions, 91.4% F1) are presented without data, error bars, implementation details, or description of how the taxonomy and verification rules were developed or validated, which directly undermines assessment of the central empirical claims.

Authors: We acknowledge the need for greater transparency in reporting the performance metrics and the development process. In the revision, we will include error bars for all key metrics, provide implementation details in an expanded methods section, and describe the iterative development and validation of the six-type taxonomy and verification rules, including how they were tested against expert annotations. The performance numbers are supported by the experiments in Section 5, and we will add references to the specific tables and figures containing the underlying data. This will allow readers to fully assess the central claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance claims are independent of inputs

full rationale

The paper presents FinGround as a three-stage empirical pipeline for financial hallucination detection and grounding. Its strongest claims (68% hallucination reduction under retrieval-equalized evaluation, 78% vs GPT-4o, 91.4% F1 for the distilled model) are reported as measured experimental outcomes on external benchmarks rather than any derivation, prediction, or first-principles result. No equations, fitted parameters, self-citations, or ansatzes appear in the abstract or described methodology that would reduce the central results to the inputs by construction. The atomic claim decomposition and six-type taxonomy are methodological components whose effectiveness is evaluated externally; they do not create a self-definitional or fitted-input loop. The paper is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the unproven effectiveness of the introduced taxonomy and type-routed verification strategies; these are domain assumptions introduced by the paper rather than derived from first principles or external benchmarks.

axioms (2)

domain assumption Financial answers can be decomposed into atomic claims that fall into one of six predefined financial types
Stage 2 classification and verification depend on this decomposition being both feasible and exhaustive.
domain assumption Type-specific verification strategies including formula reconstruction can correctly identify and correct computational errors against tables
Required to address the 43% of errors mentioned that need arithmetic re-verification.

invented entities (1)

Six-type financial taxonomy no independent evidence
purpose: Classify atomic claims to enable type-routed verification
New classification scheme introduced to handle finance-specific error types such as derived quantities and citations.

pith-pipeline@v0.9.0 · 5543 in / 1575 out tokens · 63825 ms · 2026-05-08T06:21:03.366591+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 6 canonical work pages · 3 internal anchors

[1]

An empirical investigation of statistical sig- nificance in NLP. InProceedings of the 2012 Joint Conference on Empirical Methods in Natural Lan- guage Processing and Computational Natural Lan- guage Learning, EMNLP-CoNLL 2012, July 12-14, 2012, Jeju Island, Korea, pages 995–1005. ACL. Bernd Bohnet, Vinh Q. Tran, Pat Verga, Roee Aharoni, Daniel Andor, Livi...

2012
[2]

https://arxiv.org/abs/2212.08037

Attributed question answering: Evaluation and modeling for attributed large language models. arXiv preprint, arXiv.2212.08037. Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Kenneth Huang, Bryan R. Routledge, and William Yang Wang. 2021. Finqa: A dataset of numerical reasoning over fina...

work page arXiv 2021
[3]

Distilling the Knowledge in a Neural Network

Distilling the knowledge in a neural network. arXiv preprint, arXiv.1503.02531. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025. A survey on hallucination in large lan- guage models: Principles, taxonomy, challenges, and open questions.ACM Trans. Inf. ...

work page internal anchor Pith review arXiv 2025
[4]

32 Vipula Rawte, Prachi Priya, SM Tonmoy, SM Za- man, Amit Sheth, and Amitava Das

Selfcheckgpt: Zero-resource black-box hal- lucination detection for generative large language models. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Process- ing, EMNLP 2023, Singapore, December 6-10, 2023, pages 9004–9017. Association for Computational Linguistics. Microsoft. 2024. Groundedness detection in Azure AI Content...

work page arXiv 2023
[5]

Long-form factuality in large language models

Long-form factuality in large language models. arXiv preprint, arXiv.2403.18802. Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravol- ski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David S. Rosenberg, and Gideon Mann

work page arXiv
[6]

BloombergGPT: A Large Language Model for Finance

Bloomberggpt: A large language model for finance.arXiv preprint, arXiv.2303.17564. Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling

work page internal anchor Pith review arXiv
[7]

Corrective Retrieval Augmented Generation

Corrective retrieval augmented generation. arXiv preprint, arXiv.2401.15884. Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang. 2023. Fingpt: Open-source financial large language models.arXiv preprint, arXiv.2306.06031. Mengao Zhang, Jiayu Fu, Tanya Warrier, Yuwen Wang, Tianhui Tan, and Ke-wei Huang. 2025. FAITH: A framework for assessing intrinsic tab...

work page internal anchor Pith review arXiv 2023
[8]

Total revenue was $42.3 billion

Multihiertt: Numerical reasoning over multi hierarchical tabular and textual data. InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pages 6588–6600. Association for Computational Linguis- tics. Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang,...

2022
[9]

Extract the exact assertion
[10]

Classify as: Numerical, Temporal, Entity-Attribute, Comparative, Regulatory, or Computational
[11]

For Numerical: extract value, unit, entity, time_period
[12]

claim":

For Computational: identify the implied formula or derivation. Answer: {answer} Evidence: {evidence_summary} Output (JSON): [{"claim": "...", "type": "...", "structured_fields": {...}}] The GPT-4o teacher annotation uses a two-pass process: Pass 1 generates claim-level annotations; Pass 2 verifies via a consistency check (Bohnet et al., 2022), discarding ...

2022