A large language model is a trained deep-learning model that understands and generates text in a human-like fashion.
The debate on whether large language models (LLMs) truly “know” what they are talking about is still up in the air. Yes, they do manipulate information, symbols and data in a way that seems akin to knowledge. They recognize patterns and have been trained on vast amounts of information. But the key question, based on the various philosophical perspectives, remains whether this can be classified as knowledge in the traditional sense.
LLMs can process, predict and generate based on the inputs they’ve been trained on, but they lack the crucial element of human experience—of perceiving, interacting, and experiencing the world. They don’t have beliefs or personal experiences, which we believe are two important facets of human knowledge.
LLMs seem to follow a form of rationalism where they can generate responses based on logic and statistical correlations but these responses can sometimes be flawed. Despite their sophistication, these models can and do make errors, and their ‘knowledge’ is fundamentally second-hand, an abstraction of actual human knowledge.
LLMs’ ‘knowledge’ seems to be more of an illusion or a simulacrum—close enough to pass off as the real thing in many instances, but upon closer inspection, not quite the same. This becomes problematic when such ‘knowledge’ is taken as truth without further examination, potentially leading to a spread of false information.
The implications of this debate are far-reaching. As LLMs continue to be integrated into our society, their interpretation of knowledge will play a crucial role in how we perceive truth and information. While we marvel at the capabilities of these models, it’s crucial that we don’t lose sight of the nuanced understanding that comes from human experiences, and remember to view their responses with a critical eye.
At the end of the day, while LLMs may offer us a dazzling array of insights, answers, and creations, whether they truly “know” what they’re talking about remains a question worth pondering. As we continue to interact with and develop these models, we must maintain a balance—welcoming the benefits they bring while remembering the limitations inherent in their design.
