When Ground Truth Is Not Ground Truth: Automatic Labeling for Hallucination Detection
This paper investigates the reliability of automatic labeling methods for hallucination detection in LLMs, showing that using similarity metrics like BLEURT as proxies for ground truth can be highly unreliable. Through experiments with 15 judges, 7 detectors, 2 generators, 2 QA datasets, and 5 languages, they find that detector performance varies significantly with judge choice. Human validation shows QGen-9B achieves the lowest misclassification rate (12.86%) while BLEURT and BLEURT-n exceed 47%, highlighting the need for more factuality-aware judging methods.
Why it matters: Provides concrete evidence and benchmarks on the pitfalls of common automatic labeling practices, offering actionable insights for improving hallucination detection evaluation.