Knowledge-Instruct: Effective Continual Pre-training from Limited Data using Instructions

• URL: http://arxiv.org/abs/2504.05571v1
• Date: Tue, 08 Apr 2025 00:00:36 GMT
• Title: Knowledge-Instruct: Effective Continual Pre-training from Limited Data using Instructions
• Authors: Oded Ovadia, Meni Brief, Rachel Lemberg, Eitam Sheetrit,
• Abstract summary: We introduce Knowledge-Instruct, a novel approach to efficiently inject knowledge from limited corpora through pure instruction-tuning.<n>By generating information-dense synthetic instruction data, it effectively integrates new knowledge while preserving general reasoning and instruction-following abilities.<n>We validate its effectiveness across diverse benchmarks, including Companies, a new dataset that we release to measure knowledge injection capabilities.
• Score: 0.3749861135832072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Large Language Models (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 language models. 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.

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