Enhancing Hallucination Detection through Noise Injection

Understand

Large Language Models (LLMs) are prone to generating plausible yet incorrect responses, known as hallucinations.

• Effectively detecting hallucinations is therefore crucial for the safe deployment of LLMs.
• Recent research has linked hallucinations to model uncertainty, suggesting that hallucinations can be detected by measuring dispersion over answer distributions obtained from multiple samples drawn from a model.
• While drawing from the distribution over tokens defined by the model is a natural way to obtain samples, in this work, we argue that it is suboptimal for the purpose of detecting hallucinations.