Model Collapse
Model collapse is a phenomenon where AI models trained on AI-generated data progressively lose diversity and quality over generations. It highlights the importance of maintaining high-quality human-generated training data.
Understanding Model Collapse
Model collapse is a degenerative phenomenon where AI models trained on synthetic data generated by other AI models progressively lose diversity and accuracy over successive generations. As more AI-generated content floods the internet, future models trained on this data risk learning from distorted distributions rather than authentic human-produced content, leading to narrowing outputs and amplified errors. Research has shown that even a small proportion of synthetic data in training sets can cause tails of the original distribution to disappear, meaning rare but important patterns get lost. This creates a feedback loop where each generation of models produces increasingly homogeneous and less accurate outputs. Model collapse has implications for the long-term viability of training large language models on web-scraped data and has motivated interest in data provenance, watermarking AI-generated content, and curating high-quality human-authored training datasets.
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.