MIT researchers have developed the Self Adapting Language Models (SEAL) to enhance large language models (LLMs) by allowing them to learn continually from the information they process. Unlike conventional LLMs, which do not retain knowledge from experiences, SEAL generates synthetic training data that helps its model adjust its parameters based on user inputs. This approach aims to make AI systems more responsive and personalized, drawing a parallel to human learning methods. SEAL represents an important step towards achieving human-like intelligence in AI and improving applications like chatbots.
Modern large language models (LLMs) lack the ability to learn from experience, but MIT's SEAL offers a method for continual learning using self-generated data.
The goal is to create AI models capable of continual learning that mimic human intelligence, ultimately improving chatbots and other AI applications.
SEAL allows LLMs to generate synthetic training data based on user input, promoting ongoing updates to their parameters and improving response accuracy.
The process resembles how human students take notes and review them, with the model integrating its generated insights into its own structure for better learning outcomes.
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