Choosing a Feedback Collection Method
A company is developing an AI assistant to perform factual, objective tasks, such as summarizing technical reports and extracting specific data points. They have a large team of annotators who can be trained to verify the accuracy and completeness of the AI's responses against source documents. The company needs a simple and scalable way to collect human feedback to train a reward model. Would a method where each annotator assigns an absolute quality score (e.g., on a 1-5 scale) to each individual AI response be an appropriate choice for this scenario? Justify your answer.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.2 Generative Models - Foundations of Large Language Models
Evaluation in Bloom's Taxonomy
Cognitive Psychology
Psychology
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A team is training a reward model for a language model. They collect human feedback by presenting annotators with a single, model-generated response to a prompt and asking them to assign a quality score on a scale of 1 to 10. How does this data collection approach frame the learning task for the reward model?
Choosing a Feedback Collection Method