large language models and the extended mind

Recent research has relied on the use of fine-tuning techniques to incorporate philosophical knowledge into Large Language Models (LLMs). The present paper outlines an alternative approach to the development of such systems—one that is rooted in a technique known as Retrieval-Augmented Generation (RAG). In contrast to fine-tuning, RAG does not seek to adjust the internal parameters (or internal memory) of an LLM. Instead, RAG relies on the retrieval of information from an externally-situated store, which functions as a form of non-parametric (or external) memory. Applying this technique to the works of the contemporary philosopher Andy Clark yields Digital Andy: an LLM that is able to respond to questions about the extended mind. This serves as a practical demonstration of RAG-based techniques, highlighting how philosophical knowledge can be ‘incorporated’ into an LLM without the need for additional machine learning. But Digital Andy’s reliance on extra-systemic resources also raises questions about the scope of active externalist theorizing, encouraging us to consider Digital Andy’s status as an extended cognitive/computational system. Addressing these questions reveals some interesting points of convergence between the philosophical effort to understand the extended mind and the technological effort to build the next generation of LLMs.