Summary of College: Concept Embedding Generation For Large Language Models, by Ryan Teehan et al.
CoLLEGe: Concept Embedding Generation for Large Language Models
by Ryan Teehan, Brenden Lake, Mengye Ren
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitation of current language models in quickly learning new concepts, often requiring a time-consuming fine-tuning process. CoLLEGe (Concept Learning with Language Embedding Generation) is introduced as a novel approach to few-shot concept learning, leveraging meta-learning and flexible embeddings generated from small example sets or definitions. The primary objective is to enable language models to predict next words in forthcoming sentences, compatible with pretraining. This method succeeds in real-world scenarios like new word acquisition, definition inference, and verbal reasoning without task-specific training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps language models learn new ideas quickly! Imagine you’re trying to teach a machine how to understand a new concept. It’s hard because it needs lots of practice first. Researchers developed CoLLEGe, a special way for machines to learn about new ideas using only a few examples. They tested this method with real-world tasks like learning new words and understanding definitions. The results show that CoLLEGe is effective in teaching machines to understand new concepts without needing extra training. |
Keywords
» Artificial intelligence » Embedding » Few shot » Fine tuning » Inference » Meta learning » Pretraining
