Yoonho Chang

Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses

2024 · arXiv (Cornell University)

Mitigating hallucination issues is a key challenge that must be overcome to reliably deploy large language models (LLMs) in real-world scenarios. Recently, various methods have been proposed to detect and revise factual errors in LLM-generated …