K-means for feedback clustering

You have a dataset of feedback responses, and you've used a GPT model to calculate confidence scores for each response. To identify unusual or outlier feedback, you apply k-means clustering to the low-confidence responses.

The KMeans algorithm, reviews and confidences variables, and np library have been preloaded.

This exercise is part of the course

Reinforcement Learning from Human Feedback (RLHF)

Exercise instructions

Initialize the k-means algorithm. Set the random_state to 42 for code reproducibility.
Calculate distances from cluster centers to identify outliers as the difference between data and the corresponding cluster centers.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

def detect_anomalies(data, n_clusters=3):
    # Initialize k-means
    ____
    clusters = kmeans.fit_predict(data)
    centers = kmeans.cluster_centers_

    # Calculate distances from cluster centers
    ____
    return distances
  
anomalies = detect_anomalies(confidences)
print(anomalies)

Edit and Run Code

This exercise is part of the course

Reinforcement Learning from Human Feedback (RLHF)

AdvancedSkill Level

4.8+

Start Course for Free

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 Exercise 6: K-means for feedback clustering

Current Exercise

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