Benchmarking Large Language Models on Floating-Point Error Classification
This paper investigates the capability of Large Language Models (LLMs) to detect and classify floating-point errors statically in software code. We introduce InterFLOPBench, a benchmark of 90 C kernels with 1 130 test samples designed to evaluate LLMs across six categories of floating-point error: cancellation, comparison, division by zero, overflow, underflow and NaN, compared across 14 LLMs. The evaluation framework treats floating-point error detection as a multi-label classification problem and employs the F1-score metric to measure performance. Results demonstrate that latest models (Qwen 3 32b, Gemini 2.5 Flash, Phi 4 Reasoning, DeepSeek R1T2, and gpt-oss 20b and 120b) achieve a per...
| Authors | Lisa Taldir, Muhammad Ahmad Saeed, David Defour et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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