TerryHWong/Misbehavior-Bench
Misbehavior-Bench Misbehavior-Bench is the official benchmark dataset for the ICLR 2026 paper Detecting Misbehaviors of Large Vision-Language Models by Evidential Uncertainty Quantification. This benchmark provides a comprehensive suite of evaluation scenarios designed to characterize four distinct categories of Large Vision-Language Model (LVLM) misbehaviors: Hallucination, Jailbreak, Adversarial Attacks, and Out-of-Distribution (OOD) Failures. It serves as a foundational resource… See the full description on the dataset page: https://huggingface.co/datasets/TerryHWong/Misbehavior-Bench.
| Author / organization | TerryHWong |
|---|---|
| License (as declared) | cc-by-4.0 |
| Downloads | 222 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 6 February 2026 |
| Last updated at source | 3 March 2026 |
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