Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching

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

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.
2. Applying and Reviewing Knowledge:
• Objective: Equip the model with the ability to spontaneously extract knowledge from unseen documents while reviewing its QA skills.
3. Continual Learning:
• Focus: Ensure thorough acquisition of new knowledge by continued training on the test documents.

The framework leverages the SELF-TEACHING strategy, which presents documents as plain texts for memorization alongside a series of comprehension and self-reflection tasks.

Main Findings and Results

The SELF-TUNING framework was evaluated using three newly introduced datasets: Wiki-Newpages-2023-QA, focusing on knowledge memorization, extraction, and reasoning tasks. The evaluation revealed significant improvements across all dimensions:

1. Knowledge Memorization: The model's perplexity (PPL) scores dropped to nearly 1, indicating effective memorization of new documents.
2. Knowledge Extraction: Exact match (EM) scores increased by approximately 20%, achieving performance comparable to open-book settings.
3. Reasoning Tasks: The model demonstrated high accuracy and stability in reasoning tasks, outperforming existing methods.

Implications and Potential Applications

The SELF-TUNING framework offers several implications and potential applications:

• Enhanced Knowledge Retention: The framework consistently showed strong performance in retaining previously acquired knowledge, reducing the risk of catastrophic forgetting.
• Domain-Specific Knowledge Acquisition: The ability to systematically learn new knowledge makes this approach suitable for integrating domain-specific information into LLMs.
• Real-World Applications: The improvements in knowledge extraction and reasoning capabilities suggest that SELF-TUNING can be applied to real-world scenarios where up-to-date and accurate information retrieval is crucial.

Conclusion

The SELF-TUNING framework represents a significant leap forward in enabling LLMs to stay current with new information. By emphasizing comprehension and self-reflection, this approach not only enhances knowledge acquisition but also ensures robust knowledge retention. As we continue to explore and refine this framework, its potential applications in various domains and real-world scenarios are vast and promising.

Quote from the Research Paper

The remarkable success of this potent learning method (Feynman Technique) is often attributed to its emphasis on 'comprehension' and 'self-reflection,' rather than mere 'memorization.' This encourages our exploration into its potential application in improving LLMs’ knowledge acquisition capabilities.

Future Research

Further research will focus on applying the SELF-TUNING framework to other LLMs and exploring its effectiveness in enhancing domain-specific knowledge acquisition and mathematical reasoning capabilities.

Suggested Image/Diagram

A diagram illustrating the three stages of the SELF-TUNING framework (Learning to Absorb Knowledge, Applying and Reviewing Knowledge, and Continual Learning) with examples of self-teaching tasks.

By implementing the SELF-TUNING framework, engineers and researchers can significantly improve the accuracy and reliability of LLMs in providing up-to-date information, thereby enhancing their application in various fields.