Reading Doesn't Fill a Database, It Trains Your Internal LLM (tidbits.com)

A recent reflection on the nature of reading suggests that rather than merely filling a database of knowledge, our reading experiences train our internal large language models (LLMs). The author, in dialogue with his son, likens his cognitive processes to LLM architecture, where consuming vast amounts of text adjusts the "weights" in our minds, similar to how a machine learning model fine-tunes its understanding based on training data. This perspective offers a fresh understanding of how our interactions with information continuously shape our thinking, connections, and responses. This analogy is significant for the AI/ML community as it highlights the parallels between human learning processes and the functioning of LLMs, emphasizing the importance of diverse reading and engagement in building flexible cognitive frameworks. Much like LLMs require vast datasets for training, humans refine their understanding of the world through varied knowledge exposure—consciously or unconsciously adjusting their mental models. The author challenges the conventional notion of memory retention in reading, suggesting that even if specific details are forgotten, the cumulative effect of reading reconfigures our internal models, further bridging the gap between human cognition and machine learning paradigms.