Step-Level Annotation by Human Experts for Process Supervision
A common method for process-based supervision involves generating reasoning paths for specific problems and having human experts annotate the correctness of each individual step. These detailed annotations can then be utilized either for direct supervised training of the LLM or to develop a reward model.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.4 Alignment - Foundations of Large Language Models
Learn After
Richer Annotation Schemes for Reasoning Steps
Improving Annotation Efficiency with Active Learning
Prioritizing Annotation on Confidently Incorrect Reasoning Steps
Process-Based Reward Model as a Classification Task
Process Reward Model (PRM)
A development team is training a language model to generate step-by-step solutions to complex logic puzzles. The primary objective is to improve the model's ability to construct a valid and coherent reasoning path, not just to arrive at the correct final conclusion. The team plans to use human annotators to provide feedback on the model's generated solutions. Which of the following annotation strategies is most directly aligned with improving the model's reasoning process?
Improving an AI Math Tutor's Reasoning
Evaluating Annotation Strategies for AI Training