“`html
Large language models: mistaking shadows for reality
Large language models (LLMs) feel intelligent because they speak fluently, confidently, and at scale. But fluency is not understanding, and confidence is not perception. To grasp the real limitation of today’s AI systems, it helps to revisit an idea that is more than two thousand years old.
Plato’s Allegory of the Cave
In The Republic, Plato describes the allegory of the cave: prisoners chained inside a cave can only see shadows projected on a wall. Having never seen the real objects casting those shadows, they mistake appearances for reality, and are deprived from experiencing the real world.
Large language models live in a very similar cave.
LLMs don’t perceive the world: they read about it
LLMs do not see, hear, touch, or interact wiht reality. They are trained almost entirely on text: books, articles, posts, comments, transcripts, and fragments of human expression collected from across history and the internet. that text is their only input. Their only “experience.”
LLMs only “see” shadows: texts produced by humans describing the world. Those texts are their entire universe. Everything an LLM knows about reality comes filtered through language,written by people with varying degrees of intelligence,honesty,bias,knowlege,and intent.
Text is not reality: it is a human representation of reality. It is mediated, incomplete, biased, and wildly heterogeneous, often distorted. Human language reflects opinions, misunderstandings, cultural blind spots, and outright falsehoods. Books and the internet contain unusual insights, but also conspiracy theories, propaganda, misinformation, abuse, and sheer nonsense. When we train LLMs on “all the text,” we are not giving them access to the world. We are giving them access to humanity’s shadows on the wall.
The Implications of a Text-Based Reality
This reliance on textual data has several critical implications for LLMs:
- bias Amplification: LLMs can perpetuate and even amplify biases present in the training data. For example, research from the Brookings Institution highlights how AI systems can reflect societal biases related to gender and race.
- Lack of Common Sense: Without real-world experience, LL
Related reading