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arxiv: 2607.00856 · v1 · pith:CA3PGOMXnew · submitted 2026-07-01 · 💱 q-fin.CP · cs.LG

Shapley in Context: Explaining Financial Language with Domain Expertise

Dangxing Chen , Pengzhan Guo This is my paper

Pith reviewed 2026-07-02 01:43 UTC · model grok-4.3

classification 💱 q-fin.CP cs.LG
keywords shapley valuesexplainable aifinancial textlarge language modelsdomain knowledgemodel interpretabilitytext classification
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The pith

Shapley values yield explanations for financial language models that align with domain expertise.

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

The paper examines whether Shapley value attributions explain large language models on financial text in ways that match established financial reasoning. It combines theoretical analysis with empirical tests on text-based financial tasks to check this alignment. If the attributions prove consistent with expert knowledge, they can supply the transparency finance requires under regulation. A reader would care because general-purpose explainability tools often overlook the specialized logic that financial decisions demand. The work shows these values can also reveal how models behave in such settings.

Core claim

Through rigorous theoretical analysis and extensive empirical evaluations, Shapley values produce explanations for large language models on financial textual data that remain consistent with financial domain knowledge and supply meaningful insights into model behavior.

What carries the argument

Shapley value attributions applied to large language models processing financial text, validated for consistency against domain knowledge benchmarks.

If this is right

  • Explanations from the models become consistent with financial reasoning.
  • Insights into model behavior become available for text-based financial tasks.
  • Shapley values extend to domain-specific applications beyond general tasks.
  • Regulatory transparency needs in finance can be addressed with these attributions.

Where Pith is reading between the lines

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

  • The same consistency check could be applied to other high-stakes text domains such as legal or medical applications.
  • Model debugging in finance may improve when attributions are filtered through domain benchmarks.
  • Future tests could examine whether the alignment persists when models are fine-tuned on new financial datasets.

Load-bearing premise

The chosen financial domain knowledge benchmarks and evaluation criteria accurately reflect the expert reasoning relevant to the text tasks and models examined.

What would settle it

A financial text input where Shapley attributions assign importance that directly contradicts a standard financial principle or expert judgment on the same input.

Figures

Figures reproduced from arXiv: 2607.00856 by Dangxing Chen, Pengzhan Guo.

Figure 1
Figure 1. Figure 1: A diagram for finance-inspired axioms 4. Large-Scale Risk Attribution Based on Form 10-K In Section 3, we analyzed the baseline Shapley value through simple yet intuitive il￾lustrative examples. Building on these examples, we showed how the baseline Shapley value satisfies and preserves a collection of desirable domain knowledge-inspired ax￾ioms. In this section, we present a large-scale text-based experim… view at source ↗
Figure 2
Figure 2. Figure 2: : SVB [PITH_FULL_IMAGE:figures/full_fig_p042_2.png] view at source ↗
read the original abstract

In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications. At the same time, explainability remains critical in finance, a domain characterized by high stakes and strict regulatory requirements. Although numerous methods have been proposed to explain black box machine learning models, the majority of these approaches are designed for general purpose tasks and do not incorporate domain specific knowledge. In this work, we study the explainability of financial textual data modeled by large language models through the lens of the Shapley value. Specifically, we investigate whether Shapley based attributions align with established financial domain knowledge. Through rigorous theoretical analysis and extensive empirical evaluations, we demonstrate that Shapley values can yield explanations that are consistent with financial reasoning and can offer meaningful insights into the model's behavior in text based financial applications.

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

2 major / 0 minor

Summary. The manuscript studies the explainability of LLMs on financial textual data using Shapley values. It claims that, via rigorous theoretical analysis and extensive empirical evaluations, Shapley attributions align with established financial domain knowledge and yield meaningful insights into model behavior for text-based financial applications.

Significance. If the empirical demonstrations of alignment are robust and the chosen proxies faithfully represent regulatory or decision-relevant financial reasoning, the work would strengthen the case for domain-adapted XAI methods in high-stakes finance. The absence of any equations, task descriptions, or quantitative metrics in the provided text, however, prevents evaluation of whether this potential is realized.

major comments (2)
  1. [Abstract] Abstract: the central claim that 'Shapley values can yield explanations that are consistent with financial reasoning' rests entirely on the assertion of 'extensive empirical evaluations,' yet the abstract supplies no description of the financial text tasks, LLMs employed, domain-knowledge benchmarks, or quantitative/qualitative alignment metrics. This omission is load-bearing because the stress-test concern (whether the selected criteria accurately capture expert financial reasoning) cannot be assessed.
  2. [Abstract] Abstract (empirical evaluations paragraph): without any reported tasks, data sources, or statistical controls, it is impossible to determine whether the reported consistency constitutes evidence or merely post-hoc agreement on narrow cases. A concrete test (e.g., comparison against regulatory disclosure requirements or out-of-sample expert annotations) is needed to substantiate the claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their comments. We address the two major comments on the abstract below. The full manuscript contains the requested details on tasks, models, data, and metrics, but we agree the abstract should be revised to be more informative.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'Shapley values can yield explanations that are consistent with financial reasoning' rests entirely on the assertion of 'extensive empirical evaluations,' yet the abstract supplies no description of the financial text tasks, LLMs employed, domain-knowledge benchmarks, or quantitative/qualitative alignment metrics. This omission is load-bearing because the stress-test concern (whether the selected criteria accurately capture expert financial reasoning) cannot be assessed.

    Authors: We agree the abstract is too high-level. The full paper details the tasks (financial sentiment analysis on news and regulatory text classification), LLMs (FinBERT and other domain-adapted models), benchmarks (expert-annotated feature importance aligned with financial theory), and metrics (correlation with expert judgments plus consistency checks). We will revise the abstract to briefly include these elements and key quantitative alignment results. revision: yes

  2. Referee: [Abstract] Abstract (empirical evaluations paragraph): without any reported tasks, data sources, or statistical controls, it is impossible to determine whether the reported consistency constitutes evidence or merely post-hoc agreement on narrow cases. A concrete test (e.g., comparison against regulatory disclosure requirements or out-of-sample expert annotations) is needed to substantiate the claim.

    Authors: The body of the manuscript reports the tasks, data sources (e.g., SEC filings and financial news), statistical controls (baselines and cross-validation), and concrete tests including comparisons to regulatory disclosure requirements and out-of-sample expert annotations. To address the concern that the abstract does not convey this, we will add a concise summary of the empirical setup and tests to the abstract. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation; claims rest on external empirical validation

full rationale

The provided abstract and description contain no equations, parameter-fitting steps, self-citations, or uniqueness theorems. The central claim—that Shapley attributions align with financial domain knowledge—is asserted via 'rigorous theoretical analysis and extensive empirical evaluations' without any reduction of outputs to inputs by construction. No load-bearing step reduces to a self-definition, fitted input renamed as prediction, or author-prior ansatz. The derivation chain is therefore self-contained on the information given; absence of detectable circularity is the appropriate finding rather than an intermediate score.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities are identifiable from the provided text.

pith-pipeline@v0.9.1-grok · 5659 in / 1003 out tokens · 21725 ms · 2026-07-02T01:43:45.571436+00:00 · methodology

discussion (0)

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Reference graph

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