Hallucination Evaluation
12 papers with code • 0 benchmarks • 1 datasets
Evaluate the ability of LLM to generate non-hallucination text or assess the capability of LLM to recognize hallucinations.
Benchmarks
These leaderboards are used to track progress in Hallucination Evaluation
XinhuaHallucinations
Most implemented papers
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
ictnlp/truthx
•
•
During inference, by editing LLM's internal representations in truthful space, TruthX effectively enhances the truthfulness of LLMs.
PhD: A Prompted Visual Hallucination Evaluation Dataset
The rapid growth of Large Language Models (LLMs) has driven the development of Large Vision-Language Models (LVLMs).
