How can we identify/detect hallucinations in Large Language Models (LLMs)?

Question

Hallucinations in LLMs refer to instances where the model generates responses that are factually incorrect, nonsensical, or unrelated to the input prompt. These hallucinations stem from the probabilistic nature of language models, which generate outputs based on learned patterns from extensive datasets rather than genuine understanding.

Detecting hallucinations poses a significant challenge for developers working with AI systems. Unlike traditional software defects, hallucinations add an element of unpredictability and complexity, making them harder to diagnose and rectify.

Despite their remarkable capabilities in natural language processing, LLMs often suffer from the problem of hallucination. These hallucinations can range from benign factual errors to potentially harmful fabrications such as misinformation and fake news.

Is there a way to detect/identify hallucinations in LLM models?

Answer 1

Looks like there's been some work into training classifiers on LLMs to detect hallucinations. In one dataset, model-agnostic classifiers got an 80% accuracy. (Note that the domain here only encompassed 3 tasks, so I'd expect some overfitting compared to how such classifiers would perform in the wild.)

Of course, in general, there's no way to detect hallucinations with 100% certainty without knowing the "correct" answer in the first place.