RealFin: How Well Do LLMs Reason About Finance When Users Leave Things Unsaid?
Pith reviewed 2026-05-16 07:03 UTC · model grok-4.3
The pith
LLMs often guess answers to financial questions that lack key premises instead of recognizing the gaps
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
REALFIN is constructed by systematically removing essential premises from exam-style financial questions while keeping the language natural and plausible. Evaluations under three formulations—answering, recognizing missing information, and rejecting unjustified options—reveal consistent performance drops when key conditions are absent. General-purpose models tend to over-commit and guess, while most finance-specialized models fail to clearly identify the missing premises.
What carries the argument
The REALFIN benchmark, which generates incomplete but linguistically plausible financial questions by premise removal to measure detection of insufficient information.
If this is right
- Performance declines when essential premises are removed from financial questions.
- General-purpose models tend to provide answers despite insufficient data.
- Finance-specialized models struggle to identify missing premises explicitly.
- Current evaluation methods overlook the need to know when a question should not be answered.
- Reliable financial models require mechanisms to reject or flag unjustified answers.
Where Pith is reading between the lines
- Deployment of LLMs for financial advice should incorporate explicit checks for information completeness before generating responses.
- The premise-removal approach could be adapted to test similar gaps in legal or medical reasoning domains.
- Training on synthetic incomplete financial examples might improve models' ability to detect missing conditions.
Load-bearing premise
That systematically removing premises from exam-style questions while keeping them linguistically plausible creates a valid test of real-world ability to detect missing information in finance.
What would settle it
A model that correctly refuses to answer or flags missing information on the majority of incomplete REALFIN items at rates comparable to human financial experts would undermine the claim of a critical gap.
read the original abstract
Reliable financial reasoning requires knowing not only how to answer, but also when an answer cannot be justified. In real financial practice, problems often rely on implicit assumptions that are taken for granted rather than stated explicitly, causing problems to appear solvable while lacking enough information for a definite answer. We introduce REALFIN, a bilingual benchmark that evaluates financial reasoning by systematically removing essential premises from exam-style questions while keeping them linguistically plausible. Based on this, we evaluate models under three formulations that test answering, recognizing missing information, and rejecting unjustified options, and find consistent performance drops when key conditions are absent. General-purpose models tend to over-commit and guess, while most finance-specialized models fail to clearly identify missing premises. These results highlight a critical gap in current evaluations and show that reliable financial models must know when a question should not be answered.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces REALFIN, a bilingual benchmark for evaluating LLMs on financial reasoning when essential premises are omitted from exam-style questions while preserving surface plausibility. Models are tested under three formulations (direct answering, missing-information recognition, and option rejection), with reported consistent performance drops in the absence of key conditions. General-purpose models are observed to over-commit and guess, while most finance-specialized models fail to identify missing premises, highlighting a gap in current evaluations and the need for models that know when a question should not be answered.
Significance. If substantiated with fuller methods and statistical controls, the work would be significant for financial LLM evaluation by targeting a realistic failure mode—detecting insufficient information—that is load-bearing for reliable deployment in practice. The systematic premise-removal construction and bilingual scope are strengths that could serve as a reproducible testbed for future models, directly addressing the reader's weakest assumption about premise necessity.
major comments (2)
- [§3] §3 (Benchmark Construction): The question-modification process is sketched at high level only; specific criteria for selecting 'essential premises,' the exact excision rules, and controls for linguistic plausibility are not detailed, making it impossible to verify that the resulting items constitute a valid test of missing-information detection rather than an artifact of surface changes.
- [§4] §4 (Experiments): No error bars, baseline comparisons (e.g., against simple heuristics or human performance), or statistical significance tests are reported for the claimed 'consistent performance drops,' so the central empirical claim rests on point estimates whose reliability cannot be assessed from the presented evidence.
minor comments (2)
- Table or figure captions could explicitly list the three formulations with one-sentence definitions for quick reference.
- The abstract's phrasing 'systematically removing essential premises' would benefit from a forward reference to the precise operational definition in §3.1.
Simulated Author's Rebuttal
We thank the referee for the constructive comments on benchmark construction and experimental reporting. We agree that both areas require expansion and will revise the manuscript accordingly to strengthen the presentation.
read point-by-point responses
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Referee: [§3] §3 (Benchmark Construction): The question-modification process is sketched at high level only; specific criteria for selecting 'essential premises,' the exact excision rules, and controls for linguistic plausibility are not detailed, making it impossible to verify that the resulting items constitute a valid test of missing-information detection rather than an artifact of surface changes.
Authors: We agree that the description in §3 is high-level and will expand it in the revision. Specifically, we will detail the criteria for essential premises as those that, when removed, render the question unsolvable or change the answer (e.g., specific interest rates or time periods in financial calculations). Excision rules will be specified as removing the minimal syntactic units containing the premise while preserving overall sentence plausibility through manual and automated checks. Linguistic plausibility controls will include ratings by bilingual finance experts on a 5-point scale for naturalness and coherence, with only items scoring above 4 retained. This will allow readers to verify the validity of the test items. revision: yes
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Referee: [§4] §4 (Experiments): No error bars, baseline comparisons (e.g., against simple heuristics or human performance), or statistical significance tests are reported for the claimed 'consistent performance drops,' so the central empirical claim rests on point estimates whose reliability cannot be assessed from the presented evidence.
Authors: We accept this criticism and will enhance §4 accordingly. We will report error bars using standard deviation from 5 independent runs with different random seeds for each model. Baseline comparisons will include a 'guess' baseline that always selects the first option, a heuristic based on keyword matching to common financial terms, and human performance from 10 finance experts on 100 sampled questions. Statistical significance will be assessed using paired t-tests between conditions with and without premises, with p-values reported. These changes will provide a more robust evaluation of the performance drops. revision: yes
Circularity Check
No circularity: purely empirical benchmark evaluation
full rationale
The paper introduces REALFIN as a benchmark constructed by systematically removing premises from exam-style questions and evaluates LLMs under three direct formulations (answering, missing-info recognition, option rejection). No equations, derivations, fitted parameters, or self-referential predictions exist; performance drops are measured outcomes rather than constructs that reduce to inputs by definition. The work contains no load-bearing self-citations or uniqueness claims that collapse the central result. This is a standard empirical evaluation protocol self-contained against external benchmarks.
Axiom & Free-Parameter Ledger
Forward citations
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discussion (0)
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