Detecting LLM Confabulations

Pinpointing Arbitrary Claims with Semantic Entropy Confabulation in AI describes the phenomenon where large language models (LLMs) generate fluent yet factually incorrect and arbitrary statements. These erroneous outputs are often sensitive to seemingly irrelevant details, such as the random seed used during generation. For example, an LLM might produce different answers to the same medicalContinue reading "Detecting LLM Confabulations"

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