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Liang-Jiaying / mock-interview-capstone-project

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Review AI Summer W1 S1 #16

Closed Liang-Jiaying closed 1 year ago

Liang-Jiaying commented 1 year ago
  1. Take notes.
  2. Search and learn some terminologies.
  3. List questions that I want to ask.
Liang-Jiaying commented 1 year ago

Key words to learn:

Liang-Jiaying commented 1 year ago
Liang-Jiaying commented 1 year ago

RLHF (from chatgpt 4):

Prompt: Act as an expert in Transformers. Explain RLHF (reinforcement learning on human feedback) to me as a new learner.

Output: Absolutely! Let's dive into the world of Transformers and explore RLHF (Reinforcement Learning from Human Feedback).

Setting the Stage: Transformers

To begin, Transformers are a class of deep learning models primarily used in NLP (Natural Language Processing). They've powered several breakthroughs in recent years, like BERT, GPT, and more. The key innovation in Transformers is the "attention mechanism", which allows the model to focus on different parts of the input data with varying intensity, akin to how humans pay attention to different parts of a sentence when understanding or generating text.

Introduction to RLHF

Reinforcement Learning from Human Feedback (RLHF) is a training technique, particularly suited for Transformers, that combines supervised fine-tuning and reinforcement learning to improve model behavior.

Key Components:

  1. Human Feedback: Instead of relying solely on traditional datasets, we gather feedback directly from humans. This can be in the form of comparisons, rankings, or direct feedback on model-generated outputs.

  2. Fine-tuning via Proximal Policy Optimization: Using human feedback, a reward model is constructed. The Transformer model (like GPT variants) is then fine-tuned using a reinforcement learning algorithm called Proximal Policy Optimization (PPO).

How RLHF Works:

  1. Data Collection: Initially, model responses to a set of prompts are collected. Human AI trainers might rank different model-generated responses based on quality or provide new, ideal responses.

  2. Reward Model Creation: From this feedback, a reward model is constructed. This model quantifies how good a particular response is based on the feedback data.

  3. Fine-tuning with PPO: Using the reward model as a guide, Proximal Policy Optimization (a reinforcement learning algorithm) is applied to fine-tune the Transformer model. The idea is to optimize the model's parameters so its outputs align better with the human feedback.

  4. Iterative Process: This process of gathering feedback, creating a reward model, and fine-tuning using PPO can be iteratively done multiple times to continuously refine the model.

Importance:

  1. Safety and Reliability: RLHF helps in creating models that are safer and more useful out-of-the-box, reducing harmful or nonsensical outputs.

  2. Addressing Bias: By carefully curating the feedback process, RLHF can also be used to reduce unwanted biases in model outputs.

An Analogy:

Imagine you're trying to train a pet. Traditional supervised learning is like giving the pet a treat whenever it does something right according to a pre-defined rule book. RLHF, on the other hand, is like having a group of pet trainers observe the pet, discuss amongst themselves what's desirable behavior, and then reward the pet accordingly. Over time, the pet gets a clearer picture of what's expected and starts behaving more in line with the trainers' collective feedback.

Conclusion:

RLHF is a blend of traditional supervised learning and reinforcement learning tailored for large models like Transformers. By incorporating human feedback directly into the training process, we can guide the model to produce outputs that are more aligned with human values, preferences, and safety considerations. As you delve deeper into the world of Transformers and their applications, understanding techniques like RLHF will be crucial, especially when considering the deployment of such models in real-world scenarios.

I hope that gives you a clearer understanding of RLHF! If you have more questions or need further details on any aspect, please let me know.