Challenges and Approaches for Addressing Hallucinations in Language Models

This chapter explores the challenges of training language models for evolving data and discusses various approaches to address the issue of hallucinations. It covers the misalignment between the objective of a language model and the user's belief, the use of domain adaptation, techniques for identifying and addressing hallucinations, and the impact of chopping up documents on hallucination in language models. The chapter also highlights the importance of interpretability in research to detect and mitigate hallucination, and the need for unbiased estimates in identifying optimal parameters for deep learning models.