Instruction Tuning
Instruction tuning is a fine-tuning process that trains language models to follow natural language instructions across diverse tasks. It greatly improves a model's ability to understand and execute user requests.
Understanding Instruction Tuning
Instruction tuning is a fine-tuning technique where a pre-trained language model is further trained on a diverse set of tasks formatted as natural language instructions. This process teaches the model to follow human directives more accurately, improving its ability to generalize to new tasks described in plain language. Models like FLAN, InstructGPT, and Alpaca demonstrated that instruction tuning dramatically improves a language model's helpfulness and usability compared to raw pre-training alone. The training data typically consists of thousands of instruction-response pairs spanning tasks like summarization, translation, coding, and question answering. Instruction tuning is often combined with reinforcement learning from human feedback (RLHF) to further refine the model's outputs, and it represents a key step in the pipeline for building aligned, user-friendly AI assistants.
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.