Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more
Researchers at MIT have developed a framework called Self-Adapting Language Models (SEAL) that enables large language models (LLMs) to continuously learn and adapt by updating their own internal parameters. SEAL teaches an LLM to generate its own training data and update instructions, allowing it to permanently absorb new knowledge and learn new tasks.
This framework could be useful for enterprise applications, particularly for AI agents that operate in dynamic environments, where they must constantly process new information and adapt their behavior.
The challenge of adapting LLMs
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.
Currently, when faced with a new task, LLMs typically learn from data “as-is” through methods like finetuning or in-context learning. However, the provided data is not always in an optimal format for the model to learn efficiently. Existing approaches don’t allow the model to develop its own strategies for best transforming and learning from new information.
“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, a coding assistant might need to internalize a company’s specific software framework, or a customer-facing model might need to learn a user’s unique behavior or preferences over time.”
In such cases, temporary retrieval falls short, and the knowledge needs to be “baked into” the model’s weights so that it influences all future responses.
Creating self-adapting language models
“As a step towards scalable and efficient adaptation of language models, we propose equipping LLMs with the ability to generate their own training data and finetuning directives for using such data,” the MIT researchers state in their paper.
... continue reading