Science Y'all versus yinz: Accents may say more about who we are than where we're from There's more to our speech than meets the ear. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Influences on accents go far beyond regional upbringing. Breakthroughs, discoveries, and DIY tips sent six days a week. Regional accents in the United States are far more complicated than their oversimplified stereotypes .

We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state, a key factor in countering factual hallucination and ensuring reliable application of LLMs. We observe a robust self-awareness of internal knowledge state in LLMs, evidenced by over 85% accuracy in knowledge probing. However, LLMs often fail to express their internal knowledge during generation, leading to factual hallucinations. We develop an automated hallucination annotation tool, Dreamcatcher, which merges knowledge probing and consistency checking methods to rank factual preference data. Using knowledge preference as reward, We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs. Our experiments across multiple models show that RLKF training effectively enhances the ability of models to utilize their internal knowledge state, boosting performance in a variety of knowledge-based and honesty-related tasks.