InCA stands for: In-context Continual Learning LLM Assisted by an External Continual Learner (ECL), without RAG. Imagine an LLM could continually learn, without fine-tuning, without PEFT Adapters.
This video introduces InCA, a novel method that leverages Large Language Models through continual in-context learning (C-ICL) with a unique external module (ECL) for dynamic adaptation. Current AI models struggle with continuous learning, often forgetting old tasks when learning new ones.
By using statistical models of semantic tags, InCA achieves efficient class selection, avoids catastrophic forgetting without storing any previous data, and presents a unique alternative to conventional methods based on parameter updates and even retrieval-augmented approaches. How does it work and is it better than RAG? Find out.
A key takeaway from paper is that the most important aspect is to have more effective and optimized prompts rather than focusing on gradient based optimization methods with parameter updates. It emphasizes that the proper design, modeling and formatting of prompt by the usage of the ECL method, improves the results more than any model parameter updates.
All rights w/ authors:
In-context Continual Learning Assisted by an External Continual Learner
by Saleh Momeni, Sahisnu Mazumder, Zixuan Ke, Bing Liu
from University of Illinois Chicago, USA
Intel Labs, USA and Salesforce AI Research, USA
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