Investigating Continual Pretraining in Large Language Models: Insights and Implications
This study introduces a new benchmark for continual pretraining of large language models, analyzing effects of model size and domain order on knowledge retention and transfer.
Key Findings
Methodology
Using the M2D2 dataset with 236 domains, the study pretrains GPT-2 variants and Llama2-7B sequentially, evaluating perplexity, forgetting, and transfer metrics. It compares domain orderings (similar vs. random), assesses model scale effects, and analyzes how domain similarity influences knowledge retention. The framework combines domain embeddings via Sentence-BERT, multi-metric evaluation, and ablation studies to understand the dynamics of continual pretraining in large models.
Key Results
- Continual pretraining significantly improves GPT-2 models, with GPT2-XL reducing perplexity by over 20% across domains, outperforming standalone domain adaptation. Llama2-7B shows negligible gains, indicating data size impacts effectiveness. Larger models consistently outperform smaller ones, with less forgetting observed. Random domain order enhances transfer, while related domain sequences boost specialization.
- Model scale correlates positively with performance; GPT2-XL achieves the best perplexity, while smaller models show larger fluctuations in learning and forgetting. The experiments reveal that larger models are more robust against catastrophic forgetting, and domain similarity influences transfer efficiency. Downstream task evaluations confirm that continual pretraining enhances task-specific performance, especially in semantically related domains.
- The findings demonstrate that model size, domain order, and domain similarity critically shape knowledge retention and transfer, providing insights into designing effective continual learning strategies for large models. The results also suggest that random domain sequences can mitigate forgetting and improve generalization, guiding future research in scalable continual learning.
Significance
This research establishes a comprehensive benchmark for evaluating continual pretraining in large language models, addressing gaps in understanding how model size and domain relationships influence knowledge retention and transfer. It offers practical insights for deploying models in real-world, dynamic environments, where models must adapt continuously without catastrophic forgetting. The findings support the development of more robust, scalable, and adaptable NLP systems, advancing both theoretical understanding and practical applications of continual learning in AI.
Technical Contribution
The paper introduces a multi-metric evaluation framework combining perplexity, transfer, and forgetting metrics, tailored for large-scale models and multi-domain settings. It systematically analyzes the impact of model size and domain order, revealing that larger models are inherently more resistant to forgetting. The study also innovates by integrating domain similarity measures via Sentence-BERT, guiding domain sequence design. These contributions deepen the understanding of continual learning dynamics and provide a foundation for future scalable strategies.
Novelty
This is the first comprehensive large-scale, multi-domain evaluation of continual pretraining across different model sizes, using a rich hierarchical dataset. It uniquely combines perplexity-based metrics with domain similarity analysis, revealing how model size and domain relationships influence knowledge transfer and retention. The study also demonstrates the contrasting effects of domain order, offering new insights into curriculum design for large models.
Limitations
- The experiments focus on specific models (GPT-2, Llama2-7B) and datasets, limiting generalization to other architectures or more complex, real-world domains. The computational cost of large-scale continual pretraining remains high, restricting practical deployment.
- The mechanisms of catastrophic forgetting are not deeply explored; future work should incorporate regularization or memory modules to better understand and mitigate it.
- The influence of domain similarity is complex, and current measures may oversimplify real semantic relationships, requiring more nuanced approaches.
Future Work
Future research should explore multi-task continual learning, integrating knowledge graphs and reinforcement learning to improve adaptation. Developing methods to explicitly control forgetting, such as regularization or memory-based approaches, is crucial. Extending evaluations to more diverse architectures and real-world datasets will enhance applicability. Additionally, investigating curriculum strategies based on domain similarity and transfer potential can optimize continual learning pipelines.
AI Executive Summary
The rapid growth of large language models (LLMs) like GPT-2 and Llama2 has revolutionized NLP, yet their ability to adapt continuously across diverse domains remains underexplored. Traditional fine-tuning approaches are limited in scalability and often lead to catastrophic forgetting, especially as models grow larger and more complex. To address this, the present study introduces a novel benchmark based on the comprehensive M2D2 dataset, encompassing 236 hierarchical domains, to systematically evaluate continual pretraining across different model sizes and domain sequences.
By pretraining GPT-2 variants and Llama2-7B sequentially, the authors analyze how model scale influences learning efficacy, forgetting, and knowledge transfer. The results reveal that larger models, such as GPT2-XL, consistently outperform smaller counterparts, with perplexity reductions exceeding 20% across domains. Interestingly, random domain orderings tend to enhance transfer and reduce forgetting, while related domain sequences promote specialization. These findings underscore the importance of domain curriculum design in continual learning.
The study also demonstrates that continual pretraining significantly boosts downstream task performance, especially in semantically similar domains, confirming the practical value of the approach. Conversely, smaller models like Llama2-7B show limited benefits, highlighting data size and architecture effects. The research offers a comprehensive framework for evaluating continual learning, combining perplexity, transfer, and forgetting metrics, and emphasizes the role of domain similarity and model size.
Despite these advances, challenges remain, including high computational costs, the need for better understanding of catastrophic forgetting mechanisms, and extending evaluations to more diverse architectures. Future directions include multi-task continual learning, knowledge graph integration, and curriculum optimization. Overall, this work provides a crucial step toward scalable, robust, and adaptive large language models capable of lifelong learning in dynamic environments.
Deep Analysis
Background
近年来，随着Transformer架构的普及，大规模预训练模型（如BERT、GPT系列）极大推动了NLP的发展。这些模型在海量语料上训练，显著提升了理解和生成能力，但训练成本高昂，生态和经济压力不断增加。微调技术虽取得成功，却难以应对模型在多域、多任务环境中的持续适应需求。持续学习（Continual Learning, CL）作为缓解模型遗忘和迁移瓶颈的关键技术，逐渐成为研究热点。已有研究如Gururangan等（2020）在有限域上验证了持续预训练的潜力，但缺乏大规模、多域、多模型尺度的系统评估。本文基于M2D2数据集，系统分析了GPT-2和Llama2-7B在多域连续预训练中的表现差异，填补了这一空白。
Core Problem
大模型在多域环境中面临遗忘、迁移效率低、模型规模影响不明等核心挑战。传统微调方法难以在保持性能的同时，避免知识遗失，尤其在模型规模扩大后，遗忘问题更为突出。如何设计有效的持续预训练策略，实现知识的稳固保存和高效迁移，成为亟待解决的问题。此外，模型大小对遗忘和学习效率的影响尚未系统研究，限制了实际应用的推广。解决这些问题对于推动大模型在动态环境中的应用具有重要意义。
Innovation
本研究的创新点包括：1）提出基于大规模多域数据的持续预训练新基准，系统评估不同模型规模和域顺序的影响；2）引入多维指标体系（如 perplexity、遗忘率、迁移效果），全面衡量模型能力；3）结合域相似性分析，揭示模型规模与遗忘的关系，发现大模型更抗遗忘；4）验证随机域顺序在迁移和遗忘控制中的优势，丰富持续学习策略。这些创新结合了多模型、多域、多指标的系统分析，为大模型持续学习提供了新思路。
Methodology
- �� 以预训练模型（GPT-2、Llama2-7B）为基础，利用M2D2多域数据集，逐步进行连续预训练。
- �� 设计不同域顺序（相似域与随机域），比较其对模型性能的影响。
- �� 采用 perplexity、遗忘率、迁移指标（前向/后向）以及下游任务表现，进行多维评估。
- �� 利用Sentence-BERT计算域间相似性，指导域顺序设计。
- �� 多次重复实验，分析模型规模对学习、遗忘和迁移的影响，结合统计分析验证结论。
Experiments
- �� 采用M2D2（236域，8.5亿tokens）进行模型连续预训练，模型包括GPT-2（S、M、L、XL）和Llama2-7B。
- �� 训练采用Adam优化器，批次大小16，使用DeepSpeed加速。
- �� 设计域顺序（相似与随机），评估模型在每个阶段的 perplexity 和遗忘情况。
- �� 在下游任务（如BIG-Bench）微调验证预训练效果。
- �� 多次实验确保统计显著性，分析模型大小与性能关系。
Results
- �� 持续预训练显著改善GPT-2模型（如GPT2-XL perplexity降低20%以上），Llama2-7B效果有限，说明数据量影响效果。
- �� 大模型表现优越，遗忘少，GPT2-XL在所有域中表现最佳。
- �� 随机域顺序提升迁移能力，相关域顺序增强专精，验证域关系重要性。
- �� 预训练显著提升下游任务表现，特别在语义相似域中，模型能更好地迁移和专精。
Applications
- �� 适用于企业知识库更新、智能问答、个性化推荐等场景，模型能不断适应新知识。
- �� 通过持续预训练，减少重新训练成本，提升模型在动态环境中的实用性。
- �� 长期看，有望实现跨域、多任务的通用大模型，推动智能系统的广泛应用。
Limitations & Outlook
- �� 目前实验受限于模型规模和数据集范围，实际部署需考虑算力和存储成本。
- �� 遗忘机制未深入研究，未来需结合正则化或记忆增强技术。
- �� 域关系复杂，当前相似性度量可能过于简化，需更细粒度的分析。
Plain Language Accessible to non-experts
想象你在学校学习新知识，比如数学、科学、历史。每学完一门课，就像模型接受一个新域的训练。大模型就像学习很多科目的天才，记忆力强，不容易忘记，学得快；小模型则像新手，虽然学得快，但容易忘记。随机学习不同科目顺序，就像随机域顺序，有时能帮你更灵活应对各种考试和任务。持续学习就像不断复习和总结，最终你能应对各种挑战，做出好成绩。这就像模型不断吸收新知识，保持旧的，变得越来越厉害。
ELI14 Explained like you're 14
想象你在学校里学新东西，比如数学、科学、历史。每次学完一门课，你都希望记住内容，还能用到未来的考试或项目。有时候，你学的顺序很重要，比如先学数学，再学物理，这样理解会更顺畅。有时候，随机学一些不同的科目，也能帮你变得更灵活。大哥哥（大模型）就像学习很多科目的高手，记忆力强，不容易忘记，学得快；而小弟弟（小模型）虽然学得快，但容易忘记东西。研究发现，随机学习顺序可以让你更好地迁移知识，学到的东西也更有用。这就像模型在不断学习新知识，保持旧的，还能灵活应对各种问题。
Abstract
Continual learning (CL) in large language models (LLMs) is an evolving domain that focuses on developing efficient and sustainable training strategies to adapt models to emerging knowledge and achieve robustness in dynamic environments. Our primary emphasis is on continual domain-adaptive pretraining, a process designed to equip LLMs with the ability to integrate new information from various domains while retaining previously learned knowledge. Since existing works concentrate mostly on continual fine-tuning for a limited selection of downstream tasks or training domains, we introduce a new benchmark designed to measure the adaptability of LLMs to changing pretraining data landscapes. We further examine the impact of model size on learning efficacy and forgetting, as well as how the progression and similarity of emerging domains affect the knowledge transfer within these models. Our findings uncover several key insights: (i) continual pretraining consistently improves <1.5B models studied in this work and is also superior to domain adaptation, (ii) larger models always achieve better perplexity than smaller ones when continually pretrained on the same corpus, (iii) smaller models are particularly sensitive to continual pretraining, showing the most significant rates of both learning and forgetting, (iv) continual pretraining boosts downstream task performance of GPT-2 family, (v) continual pretraining enables LLMs to specialize better when the sequence of domains shows semantic similarity while randomizing training domains leads to better transfer and final performance otherwise. We posit that our research establishes a new benchmark for CL in LLMs, providing a more realistic evaluation of knowledge retention and transfer across diverse domains.