Large Language Models (LLMs) are rapidly becoming an integral part of our digitally connected world. However, common misconceptions about how they work may impede a true understanding of their functionality and limitations.
An LLM is not a program nor a knowledge base, neither does it tap into an existing database of knowledge. Instead, it operates more like a sophisticated statistical representation of knowledge. LLMs, such as ChatGPT, are trained on hundreds of billions of parameters which they then convert into statistical patterns. Rather than possessing any inherent ‘knowledge’, an LLM utilizes patterns of knowledge to predict answers.
Undoubtedly, this delineation can frequently be blurred, leading to the misconceptions about LLMs. For instance, the sheer number of computations—between 70 to 100 billion—that an LLM can process within seconds might lead one to believe it functions like a conventional computer program. Likewise, it’s easy to misconstrue an LLM as a knowledge base given that it is trained on practically the entire internet. Moreover, the fact that such a vast array of parameters can be stored on a drive as small as 100GB only adds to the enigma.
At their core though, LLMs are more about probability than they are about data storage or retrieval. The vast number of parameters they employ help them predict outcomes or generate responses based on statistical likelihood. So, when an LLM is asked a question, it does not ‘know’ the answer in the traditional sense. Instead, it uses its stored statistical patterns derived from massive amounts of data to predict an answer that seems most likely.
In addition to misconceptions about how LLMs function, there’s also a general lack of understanding about the limitations that these models face. One primary limitation is the model’s reliance on statistics rather than inherent ‘knowledge’. This doesn’t only imply a cap on accuracy, but also implies that any responses generated would be devoid of any ‘conscious understanding’ on the part of the model, as its responses are solely based on statistical patterns.
Another challenge relates to ambiguity. As these models predict based on statistical patterns, they might struggle to decipher or respond adequately to complex or ambiguous queries where statistical likelihood cannot guarantee a correct response.
Understanding these fundamental aspects of how LLMs work and their inherent limitations is crucial for anyone working with or intending to work with Large Language Models. It tempers expectations, aids in identifying potential problem areas, and ultimately provides a clearer picture of the capabilities and constraints of this powerful technology.
