CoLLEGe: Concept Embedding Generation for Large Language Models (2403.15362v2)

Abstract: Current LLMs 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 LLMs. 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 LLM to make next word predictions in forthcoming sentences, making it compatible with LLM 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/

• The paper introduces CoLLEGe, a meta-learning framework that rapidly generates flexible concept embeddings from limited examples.
• It employs techniques like example buffering, negative sampling, and knowledge distillation to improve performance on definition inference and verbal reasoning tasks.
• Experimental results demonstrate enhanced few-shot learning and generalizability in GRE-style reasoning, slang identification, and diverse language applications.

Introducing CoLLEGe: A Meta-Learning Framework for Concept Learning in LLMs

Background and Motivation

Contemporary LLMs have reshaped our expectations from natural language processing systems, offering unprecedented capabilities in generating, understanding, and interacting with text. However, one area where these models still struggle is in the rapid acquisition of new concepts, especially when introduced through a limited number of examples. Traditional few-shot learning approaches in NLP, which often rely on global word vectors, fall short in the face of the dynamic, context-sensitive embeddings within LLMs. This paper introduces CoLLEGe (Concept Learning with Language Embedding Generation), an innovative framework designed to address this shortcoming by enabling LLMs to quickly learn and integrate new concepts through a meta-learning approach.

CoLLEGe Approach

CoLLEGe stands out by allowing LLMs to learn from a small set of examples, generating flexible embeddings that encapsulate the essence of new concepts. This method extends the utility of pretrained LLMs by equipping them with the ability to adapt to new information on-the-fly, without the need for exhaustive retraining or over-reliance on in-context examples. CoLLEGe accomplishes this through a novel meta-learning objective that integrates seamlessly with the pretraining regimens of current LLMs, thus maintaining their performance on existing tasks while augmenting their conceptual understanding.

The CoLLEGe framework employs a combination of techniques, including example buffering, negative example sampling, and knowledge distillation, to produce high-quality concept embeddings. These embeddings are then applied to a range of challenging real-world tasks, demonstrating CoLLEGe's ability to facilitate the learning of new words, infer definitions, and engage in verbal reasoning without additional task-specific training.

Experimental Results

The paper presents a thorough evaluation of CoLLEGe, showcasing its effectiveness across various tasks that simulate the introduction and usage of new concepts. Significantly, CoLLEGe demonstrates strong performance in GRE-style verbal reasoning, definition inference, and slang identification tasks. These results highlight the model's ability to generalize across different contexts and to apply newly learned concepts in complex language tasks.

Implications and Future Directions

The introduction of CoLLEGe opens up new avenues for research and application in the field of generative AI and LLMs. By bridging the gap in rapid concept learning, CoLLEGe paves the way for more adaptable, efficient, and context-aware LLMs. This work not only enhances our understanding of few-shot learning in LLMs but also sets the stage for further exploration into online continual learning and the hierarchical organization of knowledge within artificial language systems.

Conclusion

CoLLEGe represents a significant step forward in the quest to make LLMs more flexible and dynamic learners. By enabling efficient few-shot concept learning, CoLLEGe extends the applicability of LLMs to scenarios where rapid adaptation to new information is crucial. As the field of generative AI continues to evolve, approaches like CoLLEGe will play a pivotal role in shaping the next generation of LLMs, capable of navigating the ever-changing landscape of human language and knowledge.