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arXiv · 2507.08203

TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs

Duygu Nur Yaldiz·Yavuz Faruk Bakman·Sungmin Kang·Alperen Öziş·Hayrettin Eren Yildiz·Mitash Ashish Shah·Zhiqi Huang·Anoop Kumar·Alfy Samuel·Daben Liu·Sai Praneeth Karimireddy·Salman Avestimehr
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Abstract

Generative Large Language Models (LLMs)inevitably produce untruthful responses. Accurately predicting the truthfulness of these outputs is critical, especially in high-stakes settings. To accelerate research in this domain and make truthfulness prediction methods more accessible, we introduce TruthTorchLM an open-source, comprehensive Python library featuring over 30 truthfulness prediction methods, which we refer to as Truth Methods. Unlike existing toolkits such as Guardrails, which focus solely on document-grounded verification, or LM-Polygraph, which is limited to uncertainty-based methods, TruthTorchLM offers a broad and extensible collection of techniques. These methods span diverse tradeoffs in computational cost, access level (e.g., black-box vs white-box), grounding document requirements, and supervision type (self-supervised or supervised). TruthTorchLM is seamlessly compatible with both HuggingFace and LiteLLM, enabling support for locally hosted and API-based models. It also provides a unified interface for generation, evaluation, calibration, and long-form truthfulness prediction, along with a flexible framework for extending the library with new methods. We conduct an evaluation of representative truth methods on three datasets, TriviaQA, GSM8K, and FactScore-Bio. The code is available at https://github.com/Ybakman/TruthTorchLM

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Duygu Nur Yaldiz, Yavuz Faruk Bakman, Sungmin Kang, Alperen Öziş, Hayrettin Eren Yildiz, Mitash Ashish Shah, Zhiqi Huang, Anoop Kumar, Alfy Samuel, Daben Liu, Sai Praneeth Karimireddy, Salman Avestimehr. 2025-07-10. TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs. https://arxiv.org/abs/2507.08203

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