Large language models do not "hallucinate" in the human sense; rather, they are next-token predictors that generate the most probable continuation of a given input. When an LLM produces an incorrect output, it is because the prompt was underspecified, leaving the model to make probable guesses rather than retrieve factual information. To improve accuracy, users should focus on providing highly specific prompts with clear constraints on inputs, outputs, and dependencies, rather than relying on vague instructions. AI
IMPACT Users must provide highly specific prompts to LLMs to avoid "guessing" and ensure accurate outputs.
RANK_REASON The item is an opinion piece by an individual expert on the nature of LLM outputs, not a release or research finding.
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