2 papers
cs.LG2026
From token probabilities to calibrated confidence: An empirical study of mathematical question answering
Avery Ma, Lorne Schell, Vin Bhaskara +1
Confidence estimation for large language models (LLMs) aims to estimate the probability that a generated answer is correct, while calibration aligns these estimates with empirical…
cs.CL2025
Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation
Ziling Cheng, Meng Cao, Leila Pishdad +2
Final-answer-based metrics are commonly used for evaluating large language models (LLMs) on math word problems, often taken as proxies for reasoning ability. However, such metrics…