Can Large Language Models Understand What They Are Saying?

Monday - January 29 2024, 09:47 UTC - 9 months ago

Large language models (LLMs) are the foundation of modern chatbots, and new research suggests they are more capable than previously thought. A mathematical theory explains how LLMs can acquire new abilities through training, providing strong evidence that they are not just parroting what they have seen before. This has significant implications for understanding the capabilities and potential impacts of AI systems.

Artificial intelligence has become more advanced in recent years, with chatbots like Bard and ChatGPT producing text that is almost indistinguishable from human writing. But do these bots really understand what they are saying? This question has been a topic of debate among researchers, with some arguing that these bots have no real understanding and are simply mimicking information they have seen during training .

However, a new theory may suggest otherwise.In 2021, Emily Bender and her colleagues published a paper that coined the term 'stochastic parrots' to describe modern chatbots powered by large language models (LLMs). They argue that these models generate text based on combinations of information they have already seen, without any reference to meaning. In essence, these models are like parrots repeating what they have heard .

This raises the important question of whether LLMs actually understand the text they generate. According to AI pioneer Geoff Hinton, this question is more than just a theoretical consideration – it has real-world implications. In a conversation with Andrew Ng, Hinton explained that until there is a consensus on this issue, it will be difficult to address potential dangers posed by LLMs.Fortunately, new research may shed some light on the matter .

Sanjeev Arora and Anirudh Goyal have developed a mathematical theory that suggests LLMs can acquire new abilities as they get bigger and are trained on more data. These abilities go beyond what was explicitly seen during training and hint at understanding.This approach has convinced experts like Hinton, who see it as strong evidence that LLMs are not just parroting what they have seen. Indeed, when Arora and his team tested some of the theory's predictions, they found that the models behaved just as expected .

But why do LLMs develop these new abilities in the first place? It's not a straightforward result of their training process, which involves predicting missing words in sentences. The models are massive artificial neural networks with many connections (or parameters), and their size is what allows them to learn from large amounts of data.At the start of training, an LLM may struggle to accurately predict missing words .

However, with each iteration and adjustment to its parameters, it gets better at producing more likely word sequences. As the training data is large and varied, the LLM is forced to make connections between words that were initially not apparent.This is where Arora and Goyal's theory comes in. They argue that as LLMs grow and obtain more parameters through training, they develop individual language-related abilities .

Furthermore, the models can then start to combine these abilities in new ways, creating combinations that were unlikely to appear in the training data.This theory has been tested and validated by experts, such as mathematician and computer scientist Sébastien Bubeck. He believes it provides a significant insight into how LLMs acquire diverse abilities and suggests that they cannot simply be mimicking what they've seen before .

In conclusion, while there is still much to learn about AI and its capabilities, this groundbreaking research is a crucial step in understanding the extent of LLMs' abilities and how they acquire them. As AI continues to advance, it's essential to have a better understanding of these systems and their potential impacts, making this research an important contribution to the field.