TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs

Abstract

TruthTorchLM is an open-source library for estimating the truthfulness of LLM outputs. It unifies over 30 truthfulness methods spanning uncertainty estimation, verification, and supervised detectors, with different trade-offs in compute, model access, and grounding requirements. The toolkit integrates with HuggingFace and LiteLLM and provides interfaces for generation, calibration, evaluation, and long-form truthfulness analysis. Benchmarking on TriviaQA, GSM8K, and FactScore-Bio demonstrates practical utility for reliable deployment workflows.

Yavuz Bakman

PhD Student in Computer Science

My research interests include Trustworthy LLM, Continual Learning and Federated Learning.