CoLLEGe: Concept Embedding Generation for Large Language Models
<|reference_start|>CoLLEGe: Concept Embedding Generation for Large Language Models: 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.<|reference_end|>
@article{teehan2024college:,
    title={CoLLEGe: Concept Embedding Generation for Large Language Models},
    author={Ryan Teehan, Brenden Lake, Mengye Ren},
    journal={arXiv preprint arXiv:2403.15362},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15362},
    primaryClass={cs.CL cs.AI}
}