In greater detail, incidental polysemanticity emerges as a consequence of the model's attempt to efficiently use its finite resources to capture the complexity of language or data. A single neuron or representation within the network may respond to multiple, often unrelated concepts because the model finds this to be an efficient way to generalize across different contexts. This can complicate the interpretability of the model, as it becomes difficult to determine the specific role of a given neuron or representation, potentially leading to unpredictable behavior in certain contexts.
Historically, the recognition of incidental polysemanticity has grown with the development and analysis of large neural networks, particularly from the mid-2010s onwards, as models like GPT, BERT, and others began demonstrating surprising emergent behaviors. This concept became more prominent as researchers delved deeper into the interpretability of neural networks and tried to unpack the internal workings of these models.
Key contributors to the understanding of incidental polysemanticity include researchers working on the interpretability of neural networks, such as those from OpenAI and other AI research institutions. These researchers have uncovered and highlighted the complex and sometimes opaque nature of neural representations within large-scale models.