Example of Using CoT in a Preference Labeling Prompt
An effective way to improve preference labeling is to incorporate a Chain-of-Thought (CoT) rationale within the prompt. For instance, the prompt could include an example that not only states a preference for one response over another but also provides a step-by-step explanation for this choice, guiding the labeler to apply similar critical reasoning.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
Ch.4 Alignment - Foundations of Large Language Models
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Example of Using CoT in a Preference Labeling Prompt
Improving a Preference Labeling Prompt
A research team is using a large language model to automatically generate preference labels for pairs of responses to user queries. They observe that for queries requiring nuanced reasoning, the model's preference labels are inconsistent and often seem arbitrary. Which of the following prompt engineering strategies would be most effective at improving the consistency and quality of the preference labels in this scenario?
Enhancing Preference Labeling with Reasoning
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Diagnosing Inconsistent Preference Labeling
A research team is creating instructions for human labelers who will be rating the quality of two different AI-generated responses to a user's query. The team wants to include an example in their instructions that not only shows a preference but also models a clear, step-by-step reasoning process to guide the labelers. Which of the following examples best accomplishes this goal?
Improving a Preference Labeling Prompt with Chain-of-Thought