Paper: arXiv 2610.33604

Authors: Dung Cao, Palaash Gang, Arjun Veluri

Abstract

We audit the scoring pipeline of FLARE, the financial task suite behind PIXIU and FinBen, and find that in a decisive fraction of cells the score is a property of the answer-extraction rule, not the model. The clearest evidence is already published: of the 34 released Headlines scores, nine equal to four decimals what the benchmark’s own code returns when no output is byte-equal to Yes, and only four clear the per-stratum majority floor. FLARE credits the first entry of the dataset card’s choices list found anywhere in a generation; the lm-evaluation-harness it forks takes the first match by position; on identical generations the two differ by up to 0.4216 accuracy, and no leaderboard states which rule produced its numbers. An unspecified rule leaves the score partially identified, and because the coarsening is multiplicity rather than missingness the interval’s width is counted rather than bounded. Across a 204-cell factorial and a 46-cell diagnostic, 10 cells are certified invariant to the rule, 4 only because almost nothing parses, so certification is paired with coverage. On Financial PhraseBank four of seven models fall below the 0.5948 majority floor under one shipped rule and clear it under the other; on three of four substring-scored tasks no open-weight model we test is distinguishable from a degenerate predictor. Reading a FLARE score needs three quantities it does not report – ambiguity rate, coverage, floor – and floorbench computes all three from generations already produced.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: no out-of-sample test; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper rigorously audits a critical component of LLM financial benchmarks, revealing significant issues with answer extraction rules. While the mathematical complexity is moderate, the empirical depth and systematic analysis of the benchmark’s pipeline are very strong. The findings have important implications for the reliability of published LLM evaluation scores in finance.

Research Flowchart

  flowchart TD
    A[Research Goal: Audit FLARE Scoring Pipeline] --> B{Key Methodology: Score Invariance Analysis};
    B --> C[Data/Inputs: FLARE Released Headlines Scores, 204-Cell Factorial, 46-Cell Diagnostic, Financial PhraseBank Generations];
    C --> D[Computational Processes: Compare FLARE vs. lm-evaluation-harness Scoring Rules, Calculate Ambiguity Rate, Coverage, Floor (floorbench)];
    D --> E{Key Finding 1: Scores Property of Answer Extraction Rule, Not Model};
    E --> F[Key Finding 2: Scores Differ by up to 0.4216 Accuracy Based on Rule];
    F --> G[Key Finding 3: Many Models Below Majority Floor under One Rule, Clear under Another];
    G --> H[Outcome: FLARE Scores Need Ambiguity Rate, Coverage, Floor for Proper Interpretation];