Knowledge-Instruct: Effective Continual Pre-training from Limited Data using Instructions (2504.05571v1)

Abstract: While LLMs acquire vast knowledge during pre-training, they often lack domain-specific, new, or niche information. Continual pre-training (CPT) attempts to address this gap but suffers from catastrophic forgetting and inefficiencies in low-data regimes. We introduce Knowledge-Instruct, a novel approach to efficiently inject knowledge from limited corpora through pure instruction-tuning. By generating information-dense synthetic instruction data, it effectively integrates new knowledge while preserving general reasoning and instruction-following abilities. Knowledge-Instruct demonstrates superior factual memorization, minimizes catastrophic forgetting, and remains scalable by leveraging synthetic data from relatively small LLMs. Additionally, it enhances contextual understanding, including complex multi-hop reasoning, facilitating integration with retrieval systems. We validate its effectiveness across diverse benchmarks, including Companies, a new dataset that we release to measure knowledge injection capabilities.

Knowledge-Instruct: A Novel Approach for Efficient Knowledge Injection in LLMs

The paper "Knowledge-Instruct: Effective Continual Pre-training from Limited Data using Instructions" presents an innovative methodology designed to enhance the knowledge acquisition capabilities of LLMs in scenarios where data is sparse or domain-specific. This approach addresses the inherent challenge LLMs face in acquiring new or niche knowledge due to their dependence on extensive, general datasets during the pre-training phase. It introduces a method termed Knowledge-Instruct, which employs instruction-tuning to incorporate information from limited corpora while mitigating the issues of catastrophic forgetting and inefficient knowledge integration commonly associated with Continual Pre-training (CPT).

Key Contributions

The authors delineate several key advantages of Knowledge-Instruct compared to traditional CPT methods:

1. Factual Memorization Improvement: Knowledge-Instruct enhances the model's ability to accurately memorize and retrieve factual information, outperforming traditional CPT approaches.
2. Integration with Instruction Models: The approach is fully compatible with instruction-tuned models, circumventing the need for unsupervised training phases that could otherwise alter established chat templates and potentially lead to degraded performance.
3. Catastrophic Forgetting Mitigation: The methodology exhibits a reduced tendency for catastrophic forgetting, preserving the model's pre-existing capabilities while integrating new knowledge.
4. Cost Efficiency: By leveraging smaller LLMs to create synthetic instruction data, Knowledge-Instruct remains a cost-effective alternative for integrating domain-specific knowledge into LLMs.
5. Enhanced Contextual Understanding: The method improves the model's ability to interpret and reason over the retrieved context, facilitating more accurate multi-hop reasoning and retrieval-augmented generation (RAG) systems.

Empirical Evaluation

The efficacy of Knowledge-Instruct is validated through experimental comparisons with a range of existing methods across diverse datasets and models. The approach demonstrates superior performance in efficiency and stability, particularly in integrating new knowledge while maintaining the model's conversational fluency. Notably, Knowledge-Instruct excels even with limited data, positioning it as a practical solution for domain-specific applications and long-tail knowledge acquisition.

Methodological Insights

The methodology involves transforming small text corpora into compact and information-rich instructional datasets through a systematic six-step process:

1. Entity Extraction: Identification of entities within a corpus to serve as knowledge anchors.
2. Factual Extraction: Extraction of factual statements related to identified entities, ensuring comprehensive coverage of pertinent details.
3. Contextualization: Augmentation of extracted facts with context to enhance clarity and comprehension.
4. Deduplication: Removal of duplicate facts to ensure efficiency and precision in learning.
5. Paraphrasing: Generation of multiple paraphrases for each fact to enhance robustness in learning.
6. Instruction Conversion: Transformation of paraphrased facts into instruction-response pairs using pre-defined templates for supervised fine-tuning.

Implications and Future Directions

The development of Knowledge-Instruct offers significant implications for the field of artificial intelligence, particularly in advancing the capabilities of LLMs in specialized domains. Its ability to efficiently inject and retain domain-specific knowledge while minimizing computational overhead and costs could spur further research into optimizing instruction-based fine-tuning methods.

Future investigations may focus on expanding the application of Knowledge-Instruct across varied domains and exploring the potential of integrating other forms of synthetic data to enrich instruction-based learning. Additionally, examining the long-term knowledge retention capabilities and enhancing the methodological core to further minimize catastrophic forgetting remain crucial areas for advancement.

Conclusion

In summary, the Knowledge-Instruct methodology presented in this paper provides a strategic advancement in teaching LLMs to learn from small, nuanced datasets through effective instruction-tuning techniques. By addressing the critical challenge of knowledge injection from limited data, the approach stands out as a promising alternative to traditional CPT, offering practical insights and methods for scaling LLMs' understanding in specific, underrepresented domains.