Evaluating Strategies for Response Diversity
Read the following scenario and evaluate the two proposed strategies. Justify which strategy is likely to produce a more effective dataset for human feedback and explain the potential trade-offs of your chosen approach.
0
1
Tags
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.5 Inference - Foundations of Large Language Models
Evaluation in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
Examples of LLM-Generated Responses for RLHF Evaluation
Evaluating Strategies for Response Diversity
A research team is collecting data for a human feedback process. They find that their instruction-tuned model, despite sampling, consistently produces outputs that are very similar in structure and content for a given prompt. Which of the following strategies would be the most effective at introducing fundamentally different perspectives and conceptual variety into the generated responses?
Generation of Candidate Outputs from Input-Only Datasets in RLHF
A team is working on collecting a dataset for human feedback and wants to ensure a wide variety of model responses for each user request. Match each technique for increasing output diversity with the scenario that best exemplifies it.