Paper: arXiv 2602.07096

Authors: Yuyang Dai, Yan Lin, Zhuohan Xie, Yuxia Wang

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.5: no out-of-sample test; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents RealFin, an innovative bilingual benchmark evaluating LLMs on financial reasoning under incomplete information. While the mathematical complexity is low as it focuses on applied financial QA/logic, the empirical evaluation across 15 models with robust dataset curation provides high empirical value for practical AI deployment.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Evaluate LLM financial reasoning when essential premises are missing"] --> B["Data Creation:<br>Construct RealFin Benchmark"]
    
    B --> C["Data/Inputs:<br>Bilingual financial exam questions with systematically removed premises"]
    
    C --> D["Evaluation Methodology:<br>Test LLMs under 3 Formulations"]
    
    D --> E["Computational Process:<br>Assess Model Answering, Missing Info Recognition, & Option Rejection"]
    
    E --> F["Key Findings:<br>Consistent performance drops across models"]
    
    F --> G["Outcome:<br>General models over-commit/guess;<br>Finance models fail to detect missing premises"]

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3 of 98 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.