OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios
Hallucination detection is essential for the reliable deployment of large language models (LLMs). However, existing evaluations face two core challenges: inconsistent inference configuration and evaluation, and limited coverage of downstream domains and tasks. Consequently, reported detector performance is often difficult to compare, reproduce, and generalize beyond specific experimental settings. We introduce OpenHalDet, a unified benchmark for hallucination detection across diverse generation scenarios. OpenHalDet standardizes the evaluation pipeline, from prompt construction and response generation to truthfulness annotation, detector scoring, and metric computation. It supports hetero...
| Authors | Xinyi Li, Zhen Fang, Yongxin Deng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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