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Evaluating Intermediate Mistakes in Reasoning Tasks
When a Large Language Model attempts a reasoning problem, it might reach the correct final answer despite making logical errors during intermediate steps. Outcome-based approaches overlook these mistakes because they evaluate only the end result, potentially providing positive feedback for a flawed reasoning path. In contrast, process-based approaches evaluate every step individually, allowing them to identify intermediate mistakes and offer detailed guidance to correct the problem-solving process.
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Foundations of Large Language Models
Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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