HSA-DPO (Severity-Aware Direct Preference Optimization) is a method that uses fine-grained AI feedback to label hallucination severity and prioritize critical errors during the training of large vision-language models.

Authors

• Person: Not available Organization: GitHub
  EdinburghNLP/awesome-hallucination-detection - GitHub

Sources

• EdinburghNLP/awesome-hallucination-detection - GitHub github.com GitHub via serper

Referenced by nodes (1)

• Large Vision-Language Models concept