AI Learning

QR Code AI Art Generator Blend QR codes with AI Art

A few days back, a user on Reddit shared these images:

Language models (LMs), such as GPT-4, are at the forefront of natural language processing, offering capabilities that range from crafting complex prose to solving intricate computational problems. Despite their advanced functionalities, these models need fixing, sometimes yielding inaccurate or conflicting outputs. The challenge lies in enhancing their precision and versatility, particularly in complex, multi-faceted tasks. A key issue with current language models is their occasional inaccuracy and limitation in handling diverse and complex tasks. While these models excel in many areas, their efficacy could improve when confronted with tasks that demand nuanced understanding or specialized knowledge beyond their general capabilities.

In the evolving landscape of natural language processing (NLP), the ability to grasp and process extensive textual contexts is paramount. Recent advancements, as highlighted by Lewis et al. (2021), Izacard et al. (2022), and Ram et al. (2023), have significantly propelled the capabilities of language models, particularly through the development of text embeddings. These embeddings serve as the backbone for a plethora of applications, including retrieval-augmented generation for large language models (LLMs) and semantic search. They transform sentences or documents into low-dimensional vectors, capturing the essence of semantic information, which in turn facilitates tasks like clustering, classification, and information retrieval.

Explore the concept of 'model collapse' in AI. How generative AI models deteriorate when...

Discover the phenomenon of model collapse in AI. Learn why AI needs human-generated data and how to prevent model collapse.

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.