CoLLEGe: Concept Embedding Generation for Large Language Models
Ryan Teehan Brenden Lake Mengye Ren
Current language models are unable to quickly learn new concepts on the fly,
often requiring a more involved finetuning process to learn robustly. Prompting
in-context is not robust to context distractions, and often fails to confer
much information about the new concepts. Classic methods for few-shot word
learning in NLP, relying on global word vectors, are less applicable to large
language models. In this paper, we introduce a novel approach named CoLLEGe
(Concept Learning with Language Embedding Generation) to modernize few-shot
concept learning. CoLLEGe is a meta-learning framework capable of generating
flexible embeddings for new concepts using a small number of example sentences
or definitions. Our primary meta-learning objective is simply to facilitate a
language model to make next word predictions in forthcoming sentences, making
it compatible with language model pretraining. We design a series of tasks to
test new concept learning in challenging real-world scenarios, including new
word acquisition, definition inference, and verbal reasoning, and demonstrate
that our method succeeds in each setting without task-specific training.