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The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
An empirical evaluation shows automated labeling methods in LLM hallucination detection benchmarks introduce systematic labeling errors.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 7 October 2026
- Collected by OneBench
- 8 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2610.08026v1 Announce Type: new Abstract: In recent years, several methods for detecting when large language models (LLMs) hallucinate have been developed. These methods are often…
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