Qwen's FinIndices Benchmark Exposes Severe Bottlenecks in LLM Financial Reasoning
Qwen · hf · 2026-08-05
The Qwen team introduces FinIndices, a large-scale benchmark designed to evaluate LLMs' data-processing fidelity over uncropped financial statements (up to 32K tokens).
The evaluation uncovers two severe vulnerabilities in LLMs:
- Knowledge Bottleneck: Despite memorizing formulas during pre-training, models rely on fragile pattern matching. Removing explicit formula hints causes performance to collapse (e.g., Gemini drops from 70.70% to 38.22% on table tasks).
- Structural Bottleneck: Under the intense cognitive load of generating multi-metric tables, models that excel at isolated derivations regress to shallow heuristics, like fetching incorrect adjacent columns or lazy literal arithmetic.
The study validates that Supervised Fine-Tuning (SFT) can partially restore structured logic, yielding substantial zero-hint gains.
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