[Paper Review] From Static to Dynamic: A Continual Learning Framework for Large Language Models

Mingzhe Du, Anh Tuan Luu+2 more|arXiv (Cornell University)|Oct 22, 2023

Topic Modeling4 citations

TL;DR

DynaMind is a continual learning framework that enables large language models (LLMs) to dynamically integrate new knowledge without parameter fine-tuning, using a memory manager for knowledge storage and retrieval and modular operators for inference. It significantly improves LLM performance on complex reasoning (up to 92.5% accuracy with GPT-4), credibility perception (84.5% trust ratio after 5 epochs), and knowledge manipulation, including deletion (Falcon-40B achieved 74.0/100 score).

ABSTRACT

The vast number of parameters in large language models (LLMs) endows them with remarkable capabilities, allowing them to excel in a variety of natural language processing tasks. However, this complexity also presents challenges, making LLMs difficult to train and inhibiting their ability to continuously assimilate new knowledge, which may lead to inaccuracies in their outputs. To mitigate these issues, this paper presents DynaMind, a novel continual learning framework designed for LLMs. DynaMind incorporates memory mechanisms to assimilate new knowledge and modular operators to enhance the model inference process with the newly assimilated knowledge, consequently improving the accuracies of LLMs' outputs. Benchmark experiments demonstrate DynaMind's effectiveness in overcoming these challenges. The code and demo of DynaMind are available on GitHub: https://github.com/Elfsong/DynaMind.

Motivation & Objective

• Address the static nature of large language models (LLMs), which limits their ability to continuously learn new knowledge after pretraining.
• Overcome catastrophic forgetting and hallucinations in LLMs by enabling dynamic knowledge assimilation without fine-tuning model parameters.
• Endow LLMs with human-like continual learning capabilities through a memory-augmented, modular inference architecture.
• Enable LLMs to assess knowledge credibility, update, create, and delete knowledge autonomously in long-term memory.
• Demonstrate the framework’s effectiveness across three core capabilities: complex reasoning, credibility perception, and knowledge manipulation.

Proposed method

• Introduce a three-component architecture: Inference Engine, Memory Manager, and Modular Operators, enabling dynamic knowledge integration.
• Use a First-In-First-Out (FIFO) priority queue in the Inference Engine to manage operator execution order and maintain task flow.
• Implement a memory mechanism that stores, retrieves, and updates knowledge based on contextual relevance and credibility scoring.
• Apply prompt engineering with structured templates to generate interpretable command tuples (operator, parameters) from LLM outputs.
• Employ a knowledge metabolism process that iteratively evaluates and refines knowledge credibility using counterfactual validation.
• Use external knowledge sources (e.g., WikiNews, SciFact, ComplexWebQA) to inject new knowledge and evaluate assimilation performance.

Experimental results

Research questions

• RQ1 Can a parameter-free continual learning framework enhance LLMs' ability to perform complex, multi-hop reasoning with dynamically acquired knowledge?
• RQ2 To what extent can DynaMind autonomously assess and improve the credibility of stored knowledge in long-term memory?
• RQ3 How effectively can DynaMind support the creation, updating, and deletion of knowledge without modifying LLM parameters?
• RQ4 Does the framework mitigate hallucinations and improve factual consistency in LLM outputs through memory-augmented reasoning?
• RQ5 How does DynaMind’s performance compare across different LLMs (e.g., GPT-4, Falcon-40B, Llama-30B) in continual learning tasks?

Key findings

• With DynaMind, OpenAI GPT-4 achieved 92.5% accuracy on complex knowledge-driven reasoning (ComplexWebQA), up from 16.0% without external memory.
• After 5 epochs of knowledge metabolism, GPT-4 correctly identified original statements over counterfactuals in 84.5% of cases, demonstrating strong credibility perception.
• Falcon-40B outperformed GPT-4 in knowledge deletion, achieving a 74.0/100 score—higher than GPT-4’s 71.5—indicating superior knowledge pruning capability.
• DynaMind enabled Falcon-40B to achieve 85.0% accuracy on knowledge reasoning tasks, outperforming its base accuracy of 17.5% without memory augmentation.
• The framework reduced hallucinations by allowing LLMs to recall and reason with stored, contextually relevant knowledge during inference.
• Even with a four times smaller model size, Falcon-40B matched GPT-3.5’s performance in credibility perception (72.5% vs. 48.5% at 3 epochs), showing efficiency gains.