Low confidence

In this exercise, you'll be working with a reward model to assess how confidently it classifies input text and to filter out predictions that lack reliability. The goal is to evaluate the model's ability to generate predictions and to apply a confidence threshold to ensure that only high-confidence predictions are considered valid.

The probability distributions for each feedback text (prob_dists) and feedback text (texts) variables, and least_confidence() function have been loaded.

Cet exercice fait partie du cours

Reinforcement Learning from Human Feedback (RLHF)

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Instructions

Define the function to filter the indices of probability distributions for which the confidence is below a given threshold.
Get the indices of the feedback comments by passing the probability distributions to the function, leaving the threshold unchanged (0.5).

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Define the filter function
def filter_low_confidence_predictions(prob_dists, threshold=0.5):
    filtered_indices = [i for i, ____ in enumerate(____) ____]
    return filtered_indices

# Find the indices
filtered_indices = ____

high_confidence_texts = [texts[i] for i in filtered_indices]
print("High-confidence texts:", high_confidence_texts)

Modifier et exécuter le code

Cet exercice fait partie du cours

Reinforcement Learning from Human Feedback (RLHF)

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This chapter introduces the basics of Reinforcement Learning with Human Feedback (RLHF), a technique that uses human input to help AI models learn more effectively. Get started with RLHF by understanding how it differs from traditional reinforcement learning and why human feedback can enhance AI performance in various domains.

Exercise 1: Introduction to RLHF Exercise 2: Text generation with RLHF Exercise 3: Classifying generated text for RLHF Exercise 4: RL vs. RLHF Exercise 5: Exploring pre-trained LLMs Exercise 6: Tokenize a text dataset Exercise 7: Fine-tuning for review classification Exercise 8: Preparing data for RLHF Exercise 9: Preparing the preference dataset Exercise 10: Extracting prompts

Discover how to set up systems for gathering human feedback in this Chapter. Learn best practices for collecting high-quality data, from pairwise comparisons to uncertainty sampling, and explore strategies for enhancing your data collection.

Exercise 1: Methods for high-quality feedback gathering Exercise 2: Understanding comparison and rating in RLHF Exercise 3: Comparing slogans for a gym campaign Exercise 4: Measuring feedback quality and relevance Exercise 5: Low confidence

Exercice en cours

Exercise 6: K-means for feedback clustering Exercise 7: Active learning Exercise 8: Implementing an active learning pipeline Exercise 9: Active learning loop

In this Chapter, you'll get into the core of Reinforcement Learning from Human Feedback training. This includes exploring fine-tuning with PPO, techniques to train efficiently, and handling potential divergences from your metrics' objectives.

Exercise 1: Reward models explored Exercise 2: Initializing the reward Exercise 3: Setting up the reward trainer Exercise 4: Training with PPO Exercise 5: Initialize the PPO trainer Exercise 6: PPO fine-tuning Exercise 7: Efficient fine-tuning in RLHF Exercise 8: Prepare for 8-bit Training Exercise 9: Train with LoRA

Explore key techniques for assessing and improving model performance in this last Chapter of Reinforcement Learning from Human Feedback (RLHF): from fine-tuning metrics to incorporating diverse feedback sources, you'll be provided with a comprehensive toolkit to refine your models effectively.

Exercise 1: Model metrics and adjustments Exercise 2: Mitigating negative KL divergence Exercise 3: Checking the reward model Exercise 4: Incorporating diverse feedback sources Exercise 5: Majority voting on multiple data sources Exercise 6: Unreliable data source identification Exercise 7: Evaluating RLHF models Exercise 8: Interpreting curves Exercise 9: Evaluating RLHF with metrics Exercise 10: Wrapping up your RLHF journey