Introduction to Self-Adapting Language Models (SEAL)
By
archon1410
A bagel you'd recommend to a friend without hedging.
Summary
The article introduces Self-Adapting Large Language Models (SEAL), a framework that enables models to self-adapt by generating their own finetuning data and update directives. SEAL allows models to produce self-edits in response to new inputs, restructuring information, specifying optimization parameters, and invoking tools for updates. It uses reinforcement learning to train models for effective self-edits, leading to lasting adaptation.
Key quotes
· 3 pulledLarge language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks, knowledge, or examples.
Through supervised finetuning (SFT), these self-edits result in persistent weight updates, enabling lasting adaptation.
Experiments on knowledge incorporation and few-shot generalization show that SEAL is a promising step toward language models capable of self-directed adaptation.
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