Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
This study demonstrates that auxiliary views (e.g., paraphrases, analogies) significantly improve knowledge acquisition in large language models, especially at larger scales.
Key Findings
Methodology
The authors designed controlled experiments analyzing the effects of repetition, paraphrasing, and auxiliary views on knowledge learning. Using models from 1B to 32B parameters, they pre-trained on datasets from arXiv, legal opinions, and medical reports, incorporating diverse knowledge reconstructions such as textbooks, Q&A, and blogs. Knowledge probes (cloze and inference tasks) evaluated the models’ understanding. Layer-wise bias and parameter change analyses elucidated the mechanisms behind auxiliary views’ effects.
Key Results
- Allocating tokens from repeated documents to auxiliary views under fixed token budgets significantly improved factual recall and reasoning accuracy, with gains exceeding 10% in models of 13B and larger, e.g., 32B models achieved over 10% higher accuracy.
- Paraphrasing benefits diminish at larger batch sizes, but auxiliary views (textbooks, Q&A, blogs) consistently outperform simple repetition, especially in larger models, indicating enhanced generalization.
- The effectiveness of auxiliary views is robust across different generation models (GPT-4, gpt-4o), confirming their broad applicability.
Significance
This work uncovers the fundamental role of diverse, multi-faceted knowledge representations—auxiliary views—in enabling large models to learn more efficiently and generalize better. It provides a mechanistic explanation for why data diversity improves performance, emphasizing the importance of conceptual and contextual variation in training data. These insights can influence future data curation strategies, especially for low-resource domains, and inform the design of more robust, generalizable AI systems.
Technical Contribution
The paper introduces a framework integrating auxiliary views into pre-training, supported by layer-wise bias and parameter compression analyses. It demonstrates how multi-view knowledge fosters better encoding, reduces parameter change, and enhances model generalization. The approach is validated across multiple model sizes and generation models, establishing a new paradigm for data augmentation and knowledge representation in large-scale language models.
Novelty
This is the first comprehensive study to quantify and mechanistically analyze how diverse, human-like auxiliary views—such as explanations, analogies, and reformulations—enhance knowledge learning in large language models. It shifts the focus from data quantity to data diversity, revealing that conceptual and contextual variation significantly boosts model capabilities, especially at scale.
Limitations
- The experiments are primarily conducted within scientific, legal, and medical domains; applicability to other fields remains to be validated.
- Auxiliary views are generated by specific models (e.g., gpt-4o), and their quality may vary with different generators, affecting robustness.
- Large model training incurs high computational costs, limiting practical deployment; optimizing efficiency remains a challenge.
Future Work
Future research should explore multi-modal auxiliary views (images, videos), develop methods for better multi-view fusion, and investigate dynamic, continual learning scenarios. Incorporating human feedback to refine auxiliary content quality and extending these insights to real-world applications like autonomous reasoning and knowledge graphs are promising directions.
AI Executive Summary
This research advances our understanding of how large language models (LLMs) acquire and encode knowledge during pre-training. Traditional approaches focus on increasing data volume and quality, but this study emphasizes the importance of data diversity—specifically, auxiliary views such as textbooks, Q&A, and blogs—that provide multiple conceptual and contextual perspectives on the same knowledge.
Through carefully controlled experiments across models ranging from 1B to 32B parameters, the authors demonstrate that reallocating tokens from repeated documents to diverse auxiliary views significantly enhances the models’ factual recall and reasoning abilities. Notably, these improvements become more pronounced as model size increases, with 13B and larger models showing over 10% gains in accuracy on knowledge probes.
A key insight is that the benefits of auxiliary views are largely independent of the generator’s strength, as models from GPT-4 to gpt-4o produce similar improvements. Layer-wise analysis reveals that auxiliary views induce biases in the middle and final layers, promoting more efficient and generalizable knowledge encoding while reducing unnecessary parameter changes. This mechanistic understanding underscores the role of conceptual diversity in fostering knowledge compression and transfer.
Overall, the findings challenge the conventional wisdom that data quantity alone drives model performance, highlighting instead the critical role of data diversity—conceptual, contextual, and structural—in shaping robust, generalizable AI systems. These insights open new avenues for designing training curricula, especially in low-resource domains, and suggest that fostering multi-view knowledge representations could be key to future breakthroughs in AI intelligence.
Deep Dive
Abstract
Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulations of knowledge, are causally helpful for learning. We design controlled experiments to isolate this. First, we confirm that repetition is necessary for acquisition and clarify that paraphrasing helps only at smaller batch sizes. Second, holding the token budget fixed, allocating tokens from document repetition to auxiliary views improves learning, counterintuitively, even for factual recall. Third, the effectiveness of auxiliary views is not contingent on the strength of the teacher model that generates them. Fourth, we identify forms of knowledge, contextual and foundational, that aid learning in the presence of prior knowledge gaps. Finally, we examine how these effects manifest mechanistically via layer-wise biases and compression. Together, our findings suggest that auxiliary representations of knowledge, which arise naturally in large pre-training corpora, are a key factor in the success of pre-training and offer a plausible explanation for why data diversity matters.