Challenges and Limitations of Language Models | Hands-on large language models pdf | Large language models course udemy | Llm dataset format | Turtles AI

The field of large language models (LLM) has recently reached a plateau, a “wall,” where significant progress has slowed. This phenomenon is the result of several factors, including the fact that the fundamental resources that had fueled the rapid development of the technology, such as large data sets and innovation in hardware, have been largely exploited.

Key points:

• Running out of resources to innovate: The great technological leaps of the past decades, fueled by massive datasets and hardware innovations, have reached a limit. Without new accelerants, future progress in machine learning and language models will be slow and incremental.
• The importance of datasets: Datasets such as MNIST, ImageNet, and Common Crawl have played an important role in fueling machine learning and enabling rapid developments. However, access to high-quality datasets is now more limited, with implications for LLM models.
• Hardware and GPUs as accelerators: GPUs, initially developed for graphics in video games, have revolutionized machine learning, offering incredible computational capabilities. The evolution of GPUs, combined with CUDA, has enabled the qualitative leap in LLM models.
• Limitations of proprietary datasets and human feedback: Currently, the most significant progress comes from smaller, more specific datasets, and techniques such as human feedback reinforcement learning (RLHF). However, these approaches are not without challenges, particularly regarding the quality and quantity of available data.

Machine learning models, including LLMs, are intrinsically data-driven. The availability of massive volumes of data has always been a key driver of advances in the field. Since the late 1990s, with datasets such as MNIST, ImageNet, and Common Crawl, the field has seen significant leaps. MNIST, for example, changed the ability of neural networks to recognize handwritten digits, while ImageNet provided millions of labeled images, allowing neural networks to understand complex categories of objects. Common Crawl, on the other hand, provided a massive dataset of millions of web pages, which is essential for training modern LLMs.

However, these giant datasets are no longer a never-ending source of innovation. For example, while Common Crawl is still a valuable resource, changes in web content licensing and data restrictions have reduced access to high-quality information. Furthermore, the idea that increasingly large datasets can automatically improve models is being challenged. Researchers have found that smaller, but more carefully selected, data sets can yield comparable results.

Another major accelerator for machine learning has been the use of GPUs (graphics processing units). Originally designed for rendering graphics in video games, GPUs have proven to be perfect for processing the parallel computations that are essential for machine learning. Nvidia’s innovation with CUDA has enabled GPUs to be used not only for video games but also for the intensive computations required by deep learning models.

GPUs have driven accelerated progress, but now, with increasing maturity of techniques and models, hardware is no longer the resource that makes the difference between success and failure. Access to more powerful computers is still important, but it is no longer the determining factor as it once was.

Today, developments in LLM are characterized by incremental progress rather than revolutionary leaps. Companies are now exploring new approaches such as reinforcement learning with human feedback (RLHF), which integrates user feedback to optimize models in real time. This approach is becoming increasingly relevant, but the data needed to feed it is much smaller than that required to train models like GPT-3, and it is heavily protected, making it difficult to access and share.

While LLMs still show promise in specific areas, such as improving vertical applications (e.g. satellite imagery, interactive world models), the idea of ​​“general-purpose AI” (AGI) seems further away than ever. Improvements in model efficiency are another important direction, with the cost of using models decreasing.

The field of AI is entering a maturity phase, where developments will be slower and based on incremental improvements. LLMs are reaching a plateau, where future innovations will depend on optimizing what already exists rather than on radical new discoveries.

Companies are focusing on more specific applications and techniques to improve model efficiency, while the data available for model training will become increasingly limited and high-quality, rather than vast amounts of unselected data.

Source: dbreunig.com