Cross-Task Generalization of Process Reward Models
A different strategy for mitigating the lack of step-level supervision involves capitalizing on the generalization power of Process Reward Models (PRMs). The method consists of training a PRM on a source task where detailed annotation data is abundant or easy to acquire. This pre-trained model is then transferred to a target task that lacks such data, applying its learned verification capabilities with minimal or no further fine-tuning.
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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
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A development team wants to build a model that can verify the step-by-step reasoning process for complex legal document analysis. However, creating a dataset with detailed, step-by-step human annotations for this task is prohibitively expensive and requires rare legal expertise. The team has access to a very large, existing dataset for a different task: verifying step-by-step solutions to high school mathematics problems, which is cheap to annotate. The team proposes to first train their verification model on the large math dataset and then apply it directly to the legal analysis task with minimal changes. Which statement provides the strongest justification for why this approach is a sound strategy?
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Evaluating Transfer Learning Scenarios for Process Reward Models