Non Factual

Research on non-factuality in large language models (LLMs) focuses on predicting and mitigating the generation of inaccurate or misleading information. Current efforts involve developing methods to preemptively identify potentially non-factual responses before generation, using techniques like probing hidden representations within LLMs, and creating datasets to analyze the interplay between belief, deception, and factuality in generated text. This work is crucial for improving the reliability and trustworthiness of LLMs, particularly in high-stakes applications where factual accuracy is paramount, and for developing more robust and explainable AI systems.