Rapid Development of Large Language Models

In recent years, the field of artificial intelligence has seen rapid advancements in large language models. From GPT-4 to Claude, and Kimi to DeepSeek-R1, global models are flourishing with continuous technological upgrades. It is generally believed that the progress of large models is attributed to the scale of computing power and the stacking of parameters. However, the core factor determining a model’s ability to exhibit ‘intelligent emergence’ is the structure and quality of the data it uses. Large models do not become smarter simply by ‘consuming more data’ but by ‘consuming structured and high-quality data.’ Accurately understanding ‘what kind of data large AI models need’ is crucial not only for upgrading key industrial chains in the new era of productive forces but also for national security.

Why Large Models Favor Structured Data Systems

Current mainstream large models primarily use the Transformer architecture, which is designed for natural language processing (NLP) and deep learning tasks. Its attention mechanism does not rely on the literal meaning of words but focuses on constructing a network of relationships between language units. Therefore, the model’s ability to effectively learn and generalize during training depends on whether the input data possesses a clear internal logical structure. For example, structured data such as programming code and mathematical problems inherently have strong logicality, strict grammar, and predictable functional organization. This allows the model to learn reasoning paths and planning strategies, forming a cognitive structure with execution capability.

In contrast, unstructured data that is fragmented, lacks context, and has vague logic can only train the model’s superficial language generation abilities, failing to support deep understanding and reliable output. This indicates that the ‘understanding’ behavior of large models is not an intuitive grasp of semantics but a relational construction process based on ‘structural recognition.’ Without a clear structure, models cannot extract effective reasoning paths and ultimately rely on statistical simulations, failing to achieve genuine knowledge reasoning and innovation. A clear and logically rigorous data system is the true foundation for enhancing the capabilities of large models.

Five Key Data Types Supporting Model Capabilities

Currently, the key data types relied upon by large models can be categorized into five types, each corresponding to different cognitive abilities of the models:

1. Structured Data: Such as programming code and mathematical logic problems, which form the basis for reasoning, decision-making, and task planning, supporting the model’s logical rigor in training.
2. Diverse Corpora: Including spoken language, dialects, internet expressions, and cross-cultural texts. This type of corpus enhances the model’s adaptability in real-world environments, providing broader language understanding and multi-context transfer capabilities.
3. High-Quality Texts: Encompassing news reports, academic papers, and government public reports. These texts not only have authoritative content and rigorous language but also maintain coherence, helping to improve the accuracy and professional credibility of the model’s generated content.
4. Conversational Data: Such as customer service dialogues and Q&A forums, which can train the model’s multi-turn interaction and emotional perception abilities, enhancing human-machine collaboration efficiency, especially in scenarios like government services and public welfare.
5. Cross-Modal Aligned Data: Including text-image, audio-text, and video scripts, which develop the model’s representation capabilities in multi-modal spaces, facilitating the integration of multi-modal information and serving as a key support for building intelligent systems in fields like AI-assisted education, smart healthcare, and industrial automation.

These five types of data are not isolated but interwoven in applications, constructing a complex ‘data network structure.’ For instance, in smart education scenarios, a combination of image-text materials (cross-modal) with Q&A records (conversational) and knowledge point explanations (high-quality text) can achieve comprehensive modeling of students’ cognitive paths, enhancing the model’s adaptability and personalized feedback capabilities.

Challenges Facing the Current Data Ecosystem and Future Applications

Despite the significant increase in the quantity of training data in recent years, challenges remain in constructing a high-quality, well-structured data ecosystem, which may even pose ideological risks. First, the issue of ‘structural bias’ in data is prominent; for example, the overrepresentation of code and technology-related data on the internet leads to a lack of sufficient training data for humanities subjects like history and art, resulting in limitations in understanding. Second, the issue of residual biases cannot be overlooked. Data from non-reviewed sources such as social media may contain inherent biases, and if used for training without cleansing, the model may inherit these biases, leading to inappropriate or erroneous responses in public service scenarios, which could undermine social trust. Lastly, there is a scarcity of data in ’low-resource areas.’ For instance, data on minority languages and specific industry standards (such as grassroots medical records and rural governance cases) have not been systematically integrated, restricting the deep application of AI in grassroots governance and public services.

To promote the construction of a high-quality data system aimed at the new productive forces for national development, efforts can focus on three key areas: 1) Implementing cognition-driven data design. By drawing on mechanisms of children’s language acquisition, models can be guided through ‘curriculum learning’ to master knowledge structures from basic expressions to complex reasoning in stages. 2) Strengthening data structure annotation capabilities. By incorporating annotations for causal chains, timelines, and role relationships, models can establish deeper logical networks, enhancing their ability to recognize and judge events. 3) Exploring mechanisms for AI-generated synthetic data to assist in training. Under the premise of ensuring data authenticity and effectiveness, leveraging large models to generate well-structured corpora, which are then reviewed and corrected by professionals, can achieve ‘human-machine co-training’ and break through the bottleneck of insufficient high-quality data.

High-Quality Structured Data as a New Infrastructure in the Era of New Productive Forces

Large models are not solely breakthroughs achieved through traditional methods of ‘stacking parameters and algorithms’ but are intelligent systems that grow on ‘high-quality structured data.’ The training and optimization of AI models is a systematic process that requires multi-stage collaborative advancement to continually improve performance. Utilizing large-scale unsupervised or self-supervised learning data for tasks like language modeling and image generation enables models to grasp basic understanding and generation capabilities. This phase emphasizes the diversity and scale of data; only sufficiently rich data can fully explore linguistic patterns and present the world’s diverse features. Based on pre-training, fine-tuning with specifically annotated data for particular tasks is crucial for the model’s adaptation to specific application scenarios. The accuracy and consistency of high-quality annotated data determine the model’s performance in tasks such as sentiment analysis and object recognition. When real annotated data is insufficient, data augmentation and expansion techniques play a vital role. By employing methods such as text paraphrasing and image transformation, or utilizing synthetic data generation, the breadth and depth of the training set can be expanded, enhancing model performance. As the era progresses, new data continues to emerge, and models must possess the ability for continuous learning, relying on effective data update mechanisms and online learning processes to adapt to changes in language habits and popular culture. For multi-modal large models, specialized training strategies such as joint embedding space learning and cross-modal attention mechanisms are essential to effectively utilize and integrate cross-modal data.

The future competitive focus of artificial intelligence will not be purely on the scale of model parameters but on who can first establish a data system with high structural tension and generalization capabilities. This not only relates to a country’s technological strength but also to the initiative in the high ground of scientific and technological advancement and national security. Industry application models should also transition from ‘data collectors’ to ‘intelligent architecture designers.’ Just as architects design spaces, AI engineers design ‘intelligent buildings.’ However, unlike traditional buildings, we are dealing with a self-evolving, self-generalizing ‘cognitive building’—the connections between its bricks and tiles will determine whether it can ultimately describe, understand, and even transform the world.

Therefore, designing ‘high-quality structured data’ suitable for AI models will be the focal point of future AI development competition and will undoubtedly become a crucial component of the key foundational industrial chain for national development. This requires not only the innovative efforts of AI enterprises but also the guidance and regulation of national policies.