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LLMs can learn to mimic the nuanced reasoning patterns of individual annotators by contrasting their explanations with those of others, opening the door to more personalized and scalable annotation strategies.
Spotting unfaithful reasoning in LLMs just got easier: a new method efficiently compares a model's internal computations against its stated rationale.