Timothy B. Lee (Understanding AI) explains why LLMs are limited by their inability to learn new concepts during inference, relying solely on pre-existing training data. In contrast, humans continuously learn from experiences, with the brain performing training and inference simultaneously, enabling real-time adaptation.
This limitation hinders LLMs in complex problem-solving tasks that require iterative learning, as exemplified in human approaches to mathematical problem-solving.
Introducing test-time training could allow LLMs to learn from new data during inference, enhancing adaptability. However, this integration poses challenges, including risks of overfitting and increased computational demands. Despite these challenges, enabling LLMs to learn during inference could improve performance in dynamic environments.
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