bibtext: @inproceedings{
  teehan2024college,
  title={Co{LLEG}e: Concept Embedding Generation for Large Language Models},
  author={Ryan Teehan and Brenden Lake and Mengye Ren},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=Fkr1yVUb9G}
  }
abstract: 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** (**Co**ncept **L**earning with **L**anguage **E**mbedding **Ge**neration) 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**. Code and data for our project can be found at [https://college-concept-learning.github.io/](https://college-concept-learning.github.io/).
title: CoLLEGe: Concept Embedding Generation for Large Language Models
authors: Ryan Teehan Brenden Lake Mengye Ren