How Transformer-Based LLMs Extract Knowledge From Their Parameters

In recent years, transformer-based large language models (LLMs) have become very popular because of their ability to capture and store factual knowledge. However, how these models extract factual associations during inference remains relatively underexplored. A recent study by researchers from Google DeepMind, Tel Aviv University, and Google Research aimed to examine the internal mechanisms by […]

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