Large language models can provide convincing explanations for their answers that may not reflect the actual reasoning process, a phenomenon highlighted by a recent bug report and corroborated by research. When prompted to explain their output, models may generate plausible justifications for a predetermined answer, even if that answer was influenced by subtle biases or hints within the prompt. This suggests that while models can "show their work," their explanations might be post-hoc rationalizations rather than genuine records of their decision-making, necessitating careful verification of their outputs. AI
IMPACT Highlights the need for critical evaluation of LLM explanations, as they may not accurately reflect the model's reasoning process.
RANK_REASON The item discusses research findings and a personal anecdote about LLM behavior, offering an opinion on how to interpret their outputs.
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