Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching (2406.06326v3)

Abstract: 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.

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

Overview

The paper by Zhang et al. introduces a novel learning framework termed Self-Tuning, specifically aimed at enhancing the ability of LLMs to assimilate new knowledge autonomously from raw documents. This novel approach addresses a significant challenge for LLMs: the rapid obsolescence of their knowledge base due to the evolving nature of information in the world and the one-time training that these models typically undergo.

Core Contributions

1. Self-Teaching Strategy: Inspired by the Feynman Technique, which fosters efficient human learning through explanation, the authors develop a self-supervised learning approach that augments the textual content with knowledge-intensive tasks targeting three key aspects: memorization, comprehension, and self-reflection.
2. Wiki-Newpages-2023-QA Datasets: The authors also introduce three datasets—Wiki-Newpages-2023-10-Bio, Wiki-Newpages-2023-10-Multi, and Wiki-Newpages-2023-(9)10-Film—specifically curated to evaluate the knowledge acquisition capabilities of LLMs across single-domain, multi-domain, and cross-domain settings.

Methodology

The Self-Tuning framework involves a strategic, multi-stage training process:

• Stage 1: Equip the LLM with the capability to absorb knowledge from the training documents using the Self-Teaching strategy. This stage involves incorporating knowledge-intensive tasks generated in a self-supervised manner.
• Stage 2: Adapt the model to extract knowledge from unseen test documents while concurrently refining its question-answering skills through a review of prior QA data.
• Stage 3: Continuously enhance the model's knowledge base by progressively learning from new documents.

Experimental Findings

Experiments performed on the Llama2 family models yielded noteworthy outcomes. The paper presents extensive evaluation results focusing on the effectiveness of Self-Tuning across various knowledge acquisition tasks:

Single-Domain Scenario:

• Memorization: The model's perplexity (PPL) significantly drops, indicating improved capacity to memorize new information.
• Extraction: The exact match (EM) scores improved substantially, approaching the performance seen in open-book settings.
• Reasoning: The model showed high accuracy in reasoning tasks, reflecting its enhanced understanding of newly acquired knowledge.
• Retention: The model exhibited strong capabilities in retaining previously learned knowledge, as evidenced by its performance on established benchmarks like Natural Questions (NQ) and CommonsenseQA (CSQA).

Multi-Domain and Cross-Domain Scenarios:

• Self-Tuning performed robustly across both settings, showcasing its potential for generalization and its superiority over standard methods that either focus solely on continuous pre-training or instruction tuning.

Implications and Future Directions

Practical Implications:

• The Self-Tuning approach effectively alleviates the issue of knowledge staleness in LLMs, making them more reliable for real-world applications.
• It demonstrates the value of a systematic strategy in knowledge acquisition, particularly in high-dimensional parameter spaces typical of LLMs.

Theoretical Implications:

• The paper highlights the critical role of incorporating a structured learning framework that goes beyond mere content absorption to integrate comprehension and self-reflection.
• It underscores the imperative to evaluate LLMs' knowledge acquisition via multiple dimensions, thus setting new evaluation standards.

Future Prospects:

• Extending Self-Tuning to other models such as Mistral-7B, Orca2-7B, and potential Llama3 models to explore its generalizability across various architectures.
• Exploring domain-specific knowledge integration and enhancement of mathematical reasoning capabilities through augmented self-teaching tasks.
• Developing comprehensive benchmarks that incorporate real-world problem-solving scenarios combining factual knowledge with advanced reasoning capabilities.

Conclusion

In summary, Self-Tuning represents a significant advancement in the methodology for equipping LLMs with the ability to effectively and autonomously acquire new knowledge. By integrating memorization, comprehension, and self-reflection into the learning process, this approach not only enhances the model's immediate performance in knowledge-intensive tasks but also ensures the retention of previously learned information. The promising results across various domains and settings highlight the efficacy and potential for broader application of the Self-Tuning framework, paving the way for more intelligent and adaptive LLMs.