Original Paper: https://arxiv.org/abs/2406.06326

By: Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Yipeng Zhang, Haitao Mi, Helen Meng

Abstract:

Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training and the constantly evolving nature of the world. To keep LLMs current, existing approaches typically involve continued pre-training on new documents. However, they frequently face difficulties in extracting stored knowledge. Motivated by the remarkable success of the Feynman Technique in efficient human learning, we introduce Self-Tuning, a learning framework aimed at improving an LLM's ability to effectively acquire new knowledge from raw documents through self-teaching. Specifically, we develop a Self-Teaching strategy that augments the documents with a set of knowledge-intensive tasks created in a self-supervised manner, focusing on three crucial aspects: memorization, comprehension, and self-reflection. In addition, we introduce three Wiki-Newpages-2023-QA datasets to facilitate an in-depth analysis of an LLM's knowledge acquisition ability concerning memorization, extraction, and reasoning. Extensive experimental results on Llama2 family models reveal that Self-Tuning consistently exhibits superior performance across all knowledge acquisition tasks and excels in preserving previous knowledge.

Summary Notes

Revolutionizing Knowledge Acquisition in LLMs: Introducing the SELF-TUNING Framework

Introduction

As engineers, we often rely on the cutting-edge capabilities of Large Language Models (LLMs) to provide insights and solve complex problems. However, one persistent challenge with LLMs is their inability to stay current with the ever-evolving world due to their one-time training nature. This blog post delves into an innovative approach called SELF-TUNING to address this issue, enabling LLMs to effectively acquire and internalize new knowledge through self-teaching.

Key Methodologies

SELF-TUNING is inspired by the Feynman Technique, a potent learning method that emphasizes comprehension and self-reflection over mere memorization. The framework is designed to enhance LLMs' ability to learn from raw documents by incorporating three stages:

1. Learning to Absorb Knowledge:
   • Training Data: A combination of training documents and associated QA data.
   • Self-Teaching Tasks: Tasks such as summarization, gist identification, and natural language inference (NLI) are presented in a self-supervised manner to facilitate deep learning.