Reinforcement Learning from Human Feedback
RLHF is a training technique that uses human preferences to fine-tune AI models, aligning their outputs with human values and expectations. RLHF is key to making language models helpful, harmless, and honest.
Understanding Reinforcement Learning from Human Feedback
Reinforcement Learning from Human Feedback (RLHF) is a training methodology that aligns large language models with human preferences by using human evaluations to train a reward model, which then guides the language model's optimization through reinforcement learning. The process typically involves three stages: supervised fine-tuning on high-quality demonstrations, training a reward model on human comparisons of model outputs, and optimizing the language model against this reward model using algorithms like Proximal Policy Optimization (PPO). RLHF was instrumental in transforming GPT-3 into ChatGPT, dramatically improving the model's helpfulness, honesty, and safety. The technique addresses the gap between the next-token prediction objective used in pre-training and the actual qualities humans value in AI responses. Alternatives and extensions include Direct Preference Optimization (DPO), which simplifies the pipeline by eliminating the separate reward model, and Constitutional AI, which uses AI feedback alongside human feedback to scale the alignment process.
Category
Generative AI
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Chain of Thought
Chain of thought is a prompting technique that encourages large language models to break down complex reasoning into intermediate steps. This approach significantly improves performance on math, logic, and multi-step reasoning tasks.
ChatGPT
ChatGPT is an AI chatbot developed by OpenAI that uses large language models to generate human-like conversational responses. It became one of the fastest-growing consumer applications in history after its launch in November 2022.
Claude
Claude is an AI assistant developed by Anthropic, designed to be helpful, harmless, and honest. It is built using Constitutional AI techniques and competes with models like GPT-4 and Gemini.
Diffusion Model
A diffusion model is a generative AI model that creates data by learning to reverse a gradual noise-adding process. Diffusion models power state-of-the-art image generation systems like Stable Diffusion and DALL-E.
Discriminator
A discriminator is the component of a GAN that learns to distinguish between real and generated data. It provides feedback to the generator, creating an adversarial training dynamic that improves output quality.
Few-Shot Prompting
Few-shot prompting provides a language model with a small number of input-output examples in the prompt to demonstrate the desired task format. This technique helps models understand task requirements without any fine-tuning.
Foundation Model
A foundation model is a large AI model trained on broad data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Gemini, and DALL-E are examples of foundation models that serve as bases for specialized applications.
GAN
A GAN (Generative Adversarial Network) is a generative model consisting of two competing neural networks — a generator and a discriminator. GANs produce realistic synthetic data by training these networks in an adversarial game.