Title: Interpretable Probability Estimation with LLMs via Shapley Reconstruction
Primary area: interpretability and explainable AI
Keywords: Large language models, Probability estimation, Interpretability and Trustworthiness

Abstract: Large Language Models (LLMs) demonstrate potential to estimate the probability of uncertain events, by leveraging their extensive knowledge and reasoning capabilities. This ability can be applied to support intelligent decision-making across diverse fields, such as financial forecasting and preventive healthcare. However, directly prompting LLMs for probability estimation faces significant challenges: their outputs are often noisy, and the underlying predicting process is opaque. In this paper, we propose **PRISM: Probability Reconstruction via Shapley Measures**, a framework that brings transparency and precision to LLM-based probability estimation. PRISM decomposes an LLM’s prediction by quantifying the marginal contribution of each input factor using Shapley values. These factor-level contributions are then aggregated to reconstruct a calibrated final estimate. In our experiments, we demonstrate PRISM improves predictive accuracy over direct prompting and other baselines, across multiple domains including finance, healthcare, and agriculture. Beyond performance, PRISM provides a transparent prediction pipeline: our case studies visualize how individual factors shape the final estimate, helping build trust in LLM-based decision support systems.

TL;DR: We propose a factor-level, Shapley-inspired framework for explainable and reliable probability estimation using LLMs.