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Researchers have long observed that neurons in the brain tend to be organized in clusters, with neighboring neurons often sharing similar functions. This phenomenon is also seen in the brain’s language system, where certain areas respond to different aspects of language, such as syntax (sentence structure) or semantics (meaning). However, the exact mechanisms behind this organization remain a mystery.
In an attempt to better understand how the brain organizes language processing, the researchers developed TopoLM, a new type of AI language model inspired by the brain’s spatial layout. Unlike traditional language models, TopoLM arranges its processing units in a two-dimensional space, mimicking how neurons are arranged in the brain. It combines a standard language task (predicting the next word in a sentence) with an additional goal: encouraging units that are close together in space to also have similar functions, creating clusters of units that process similar linguistic information.
