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Beyond static AI: MIT’s new framework lets models teach themselves
MIT researchers developed SEAL, a framework that lets language models continuously learn new knowledge and tasks.
While large language models have shown remarkable abilities, adapting them to specific tasks, integrating new information, or mastering novel reasoning skills remains a significant hurdle. “Many enterprise use cases demand more than just factual recall—they require deeper, persistent adaptation,” Jyo Pari, PhD student at MIT and co-author of the paper, told VentureBeat. For example, the researchers propose that an LLM could ingest complex documents like academic papers or financial reports and autonomously generate thousands of explanations and implications to deepen its understanding.
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