A well-known problem of large language models (LLMs) is their tendency to generate incorrect or nonsensical outputs, often called “hallucinations.” While much research has focused on analyzing these errors from a user’s perspective, a new study by researchers at Technion, Google Research and Apple investigates the inner workings of LLMs, revealing that these models possess a much deeper understanding of truthfulness than previously thought. This finding suggests that current evaluation methods, which solely rely on the final output of LLMs, may not accurately reflect their true capabilities. It raises the possibility that by better understanding and leveraging the internal knowledge of LLMs, we might be able to unlock hidden potential and significantly reduce errors.
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