HaluBench
Models judge whether an answer is faithful to its context across about 15,000 labeled context, question and answer triplets. It tests hallucination detectors and LLM judges.
Released with Patronus's Lynx hallucination detection model. Examples come from existing QA datasets and include finance and medicine domains.
- Made by
- Patronus AI
- Released
- July 2024
- Kind
- Hallucination rates
- Level
- University
- Scored
- Checked automatically
- Answers
- Multiple choice
- Tasks
- All public
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for HaluBench yet.
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
More in Factuality
- SimpleVQA In use Models answer 2,025 short factual questions about images across 9 tasks and 9 topics. An LLM judge grades answers against fixed references.
Qwen3.7-Plus 81.7% - HallusionBench In use Models answer 1,129 yes or no questions about 346 images, built in pairs to expose language hallucination and visual illusion. Scored by accuracy.
Keye-VL-2.0-30B-A3B 73.5% - SimpleQA Verified In use 1,000 human-verified, de-duplicated short-answer factual questions measuring parametric knowledge.
Gemini 3.1 Pro 77.5% - Kimi Hallu Bench In use Models answer hard prompts drawn from practical work tasks. Fact-checking agents score the share of claims that are hallucinated. GPT-6 Astra 4.20%
- Internal adversarially-designed factuality evaluations In use Models answer adversarial questions in nine categories such as history, math and code. Scored on agreement with human ideal responses. GPT-6 Luna 7.60%
- AA-Omniscience v1.1 In use Models answer 3,000 knowledge questions in six domains without tools and may abstain. The index adds correct answers and subtracts wrong ones.
Inkling 2 Small 16.1