TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs (2025.emnlp-demos)
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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
| Challenge: | Generative Large Language Models (LLMs) produce untruthful outputs, referred to as hallucinations, which are often referred as false positives. |
| Approach: | They propose an open-source Python library with over 30 truthfulness prediction methods. |
| Outcome: | The proposed methods span diverse trade-offs in computational cost, access level, grounding document requirements, and supervision type (self-supervised or supervised). |
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