Understanding How AI Learns: From Word Position to Meaning

The language capabilities of today's artificial intelligence systems are impressive. Systems like ChatGPT, Gemini, and others can engage in natural conversations almost as fluently as humans. However, the internal processes that lead to these results are still not fully understood.

A recent study published in the Journal of Statistical Mechanics: Theory and Experiment (JSTAT) sheds light on this mystery. It reveals that neural networks initially focus on word positions in a sentence when trained with small data sets. But as they receive more data, they shift to emphasizing word meanings. This transition occurs abruptly after crossing a critical data threshold, similar to a phase transition in physical systems. These findings provide valuable insights into the functioning of these models.

Similar to a child learning to read, a neural network first understands sentences based on word positions. But as training progresses, it transitions to prioritizing word meanings. This shift is observed in a simplified model of self-attention mechanism, a key component of transformer language models like ChatGPT and Gemini.

The study's first author, Hugo Cui, explains that neural networks can use two strategies to assess word relationships, with the position of words being the initial focus. However, once a critical data threshold is crossed, the network switches to relying on word meanings.

This shift is likened to a phase transition in physics, where the network behavior changes abruptly. Understanding this transition is crucial for improving the efficiency and safety of neural networks in the future.

The research by Hugo Cui and team, titled 'A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention,' is published in JSTAT as part of the Machine Learning 2025 special issue and NeurIPS 2024 conference proceedings.

According to the source: Mirage News.