I’ve reached the tentative conclusion that LLMs are associative memories. Well, in the Meno, Plato advances the idea that learning is, in fact, recollection, an idea which seemed very strange to me as an undergraduate philosophy major back in The Ancient Days. It doesn’t seem so strange to me now, now that I have a more sophisticated and more abstract understanding of memory. Anyhow, I decided to ask ChatGPT about the Meno and to apply the idea of anamnesis to LLMs.

Can you summarize Plato’s Meno?

Certainly! “Meno” is a Socratic dialogue written by the ancient Greek philosopher Plato. The dialogue takes place between Socrates and Meno, a young Thessalian aristocrat. The central question of the dialogue is whether virtue can be taught.

The dialogue begins with Meno asking Socrates whether virtue can be taught. Socrates responds by claiming that he does not know what virtue is, and he engages Meno in a discussion to explore its nature. Socrates uses a method of questioning called the Socratic method to guide Meno in thinking critically about virtue.

Throughout the dialogue, Socrates and Meno discuss various aspects of virtue, including whether it is a kind of knowledge and whether it can be acquired through teaching or is innate. The discussion leads to the famous “Meno’s paradox,” where Meno questions how one can seek knowledge if they don’t already know what they’re seeking.

Socrates introduces the idea of anamnesis, suggesting that learning is a process of recollection, and the soul already possesses knowledge that is remembered through questioning. The dialogue does not provide a definitive answer to whether virtue can be taught, leaving the question somewhat unresolved.

Overall, “Meno” is a philosophical exploration of the nature of virtue and the methods of acquiring knowledge, featuring the distinctive Socratic style of inquiry and dialogue.

Discuss the doctrine of anamnesis in the context of modern large language models.

The doctrine of anamnesis, as presented in Plato’s “Meno,” suggests that learning is a process of recollection, and individuals have innate knowledge that is brought to consciousness through questioning and inquiry. In the context of modern large language models, such as GPT-3, the analogy with anamnesis can be drawn in several ways:

1. Pre-existing Knowledge: Like the idea of innate knowledge in anamnesis, large language models are trained on vast amounts of data, essentially capturing a wide range of human knowledge present in texts. This pre-existing knowledge is then used to generate responses and engage in various language-based tasks.

2. Generalization from Data: Anamnesis implies that individuals can access and remember knowledge through a process of questioning. Similarly, language models generalize from the diverse data they are trained on, enabling them to respond to a wide array of questions and prompts.

3. Inference and Recall: The process of anamnesis involves recalling information from one’s innate knowledge. In the case of language models, they employ sophisticated algorithms and patterns to infer and recall information from the training data, allowing them to answer questions and provide contextually relevant information.

4. Continuous Learning: Anamnesis doesn’t just involve recalling existing knowledge but also learning through the process of questioning. Large language models, through fine-tuning and continuous exposure to new data, can adapt and learn from new information, somewhat resembling the ongoing process of anamnesis.

However, it’s important to note that there are significant differences between the human mind’s conceptualization of anamnesis and the functioning of large language models. While language models can provide impressive performances in language-related tasks, they lack consciousness, self-awareness, and true understanding. Anamnesis, as envisioned by Plato, involves a metaphysical claim about the nature of the soul, which goes beyond the capabilities of artificial intelligence models. Nevertheless, drawing parallels can help us appreciate how modern technology, in its own way, captures and utilizes vast amounts of information to generate intelligent responses.