Black-Box Hallucination Detection via Consistency Under the Uncertain Expression
This addresses the hallucination issue in LLMs for users relying on black-box APIs, offering a practical detection method without requiring internal model access.
The paper tackles the problem of detecting hallucinations in large language models (LLMs) by proposing a black-box metric based on consistency under uncertain expressions, showing it predicts factuality better than baselines using internal model knowledge.
Despite the great advancement of Language modeling in recent days, Large Language Models (LLMs) such as GPT3 are notorious for generating non-factual responses, so-called "hallucination" problems. Existing methods for detecting and alleviating this hallucination problem require external resources or the internal state of LLMs, such as the output probability of each token. Given the LLM's restricted external API availability and the limited scope of external resources, there is an urgent demand to establish the Black-Box approach as the cornerstone for effective hallucination detection. In this work, we propose a simple black-box hallucination detection metric after the investigation of the behavior of LLMs under expression of uncertainty. Our comprehensive analysis reveals that LLMs generate consistent responses when they present factual responses while non-consistent responses vice versa. Based on the analysis, we propose an efficient black-box hallucination detection metric with the expression of uncertainty. The experiment demonstrates that our metric is more predictive of the factuality in model responses than baselines that use internal knowledge of LLMs.