Open-Source Toolkit: Assessing and Mitigating Hallucination Risk in LLMs
2025-09-09
Hassana Labs has released an open-source toolkit for assessing and mitigating hallucination risk in large language models (LLMs). Without requiring model retraining, the toolkit leverages the OpenAI Chat Completions API. It creates an ensemble of content-weakened prompts (rolling priors) to calculate an upper bound on hallucination risk using the Expectation-level Decompression Law (EDFL). A decision to answer or refuse is made based on a target service-level agreement (SLA). Supporting both evidence-based and closed-book deployment modes, the toolkit provides comprehensive metrics and an audit trail for building more reliable LLM applications.