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A new study offers insights into the perplexing issue of large language model (LLM) hallucinations – instances where these models confidently produce false or misleading information. The research posits that current LLM training and evaluation methodologies inadvertently incentivize guessing rather than genuine understanding and expression of uncertainty. The team discovered that ‘hallucinations’ become commonplace when differentiating between accurate and inaccurate statements proves challenging, further exacerbated by benchmark scoring systems that favor ‘test-taking’ performance over dependable reasoning processes. The researchers suggest modifying these benchmarks to better reward trustworthiness as a potential solution. Initial discussions surrounding this research originated on Reddit’s Artificial Intelligence forum.