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Improving Feedback Collection for a Chatbot
Based on the following scenario, propose a more reliable method for collecting human feedback that involves presenting multiple chatbot responses at once. Explain how this method works and why it would likely reduce the problem of annotator disagreement.
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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
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Example of a Human Preference Ranking in RLHF
Listwise Loss from Accumulated Pairwise Comparisons
Plackett-Luce Model for Listwise Ranking
Example of Listwise Ranking in RLHF
A team is developing a language model to generate compelling short story endings. To gather human feedback, they generate four different endings for each story prompt. They are considering two feedback collection strategies:
Strategy 1: Human annotators are shown all four endings at once and asked to order them from best to worst.
Strategy 2: Human annotators are shown each of the four endings one at a time and asked to rate its quality on a scale of 1 to 10.
Based on the goal of collecting the most reliable data for model improvement, which strategy is generally more effective and why?
Improving Feedback Collection for a Chatbot
When using a listwise ranking approach to collect human feedback for a language model, the primary task for an annotator is to assign an independent numerical quality score (e.g., 1 to 10) to each of the model's generated outputs.