hallucination-leaderboard and Awesome-LVLM-Hallucination
These are ecosystem siblings: one benchmarks hallucination behavior in text-based LLMs while the other curates research resources for vision-language model hallucinations, addressing related problems across different modalities within the broader hallucination mitigation domain.
About hallucination-leaderboard
vectara/hallucination-leaderboard
Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents
Leverages Vectara's HHEM-2.3 hallucination evaluation model to score LLMs across a curated, non-public dataset of 7700+ documents spanning news, science, medicine, and legal domains with varying complexity (50–24K words). Rankings track hallucination rate, factual consistency, answer rate, and summary length, with results updated regularly as models evolve. Integrates with Hugging Face for interactive exploration and provides an open-source HHEM-2.1 variant for reproducible research.
About Awesome-LVLM-Hallucination
NishilBalar/Awesome-LVLM-Hallucination
up-to-date curated list of state-of-the-art Large vision language models hallucinations research work, papers & resources
Scores updated daily from GitHub, PyPI, and npm data. How scores work