Principled Detection of Hallucinations in Large Language Models via Multiple Testing
This addresses the issue of unreliable outputs in LLMs for users relying on accurate AI-generated content, representing an incremental improvement in detection techniques.
The paper tackles the problem of detecting hallucinations in Large Language Models by formulating it as a hypothesis testing problem and proposing a multiple-testing-inspired method, with experimental results showing robust performance against state-of-the-art methods.
While Large Language Models (LLMs) have emerged as powerful foundational models to solve a variety of tasks, they have also been shown to be prone to hallucinations, i.e., generating responses that sound confident but are actually incorrect or even nonsensical. In this work, we formulate the problem of detecting hallucinations as a hypothesis testing problem and draw parallels to the problem of out-of-distribution detection in machine learning models. We propose a multiple-testing-inspired method to solve the hallucination detection problem, and provide extensive experimental results to validate the robustness of our approach against state-of-the-art methods.