Feature Overview

This feature allows users to preference tune a model with InstructLab by providing a dataset of examples containing questions with multiple answers, along with the preferred answer. This enables the model to learn from human feedback and improve its performance over time.

Goals

• Primary User Type: Data Scientists, AI engineers

Expected functionality

• Users can provide a preference dataset containing questions, answers, and the preferred answer.
• The system will use this data to preference-tune the model's behavior, improving its performance in generating desired responses.

Requirements

• Develop an algorithm to process the preference dataset and fine-tune the model accordingly.
• Ensure the system can handle datasets of varying sizes and complexities.

Background

Reinforcement Learning from Human Feedback (RLHF) is a technique used to align AI models to human preferences by learning from human feedback. This feature will enable users to provide this feedback in the form of preference datasets.

Done

• [ ] Develop the algorithm to process the preference dataset.
• [ ] Test the system with various datasets to ensure it works as expected.

Questions to Answer

• What data formats will be accepted for the preference dataset?
• How will the system handle cases where the preferred answer is not provided in the dataset?
• What metrics will be used to evaluate the success of the RLHF fine-tuning process?

Out of Scope

• [ ] Handling complex data preprocessing tasks.

Customer Considerations

• Users should have a basic understanding of preference datasets and RLHF to effectively use this feature.
• The system should be able to handle datasets with varying levels of quality and consistency.
• Users should be aware of the potential for bias in the preference dataset and take steps to mitigate it.