Rapid Development of AI Language Models

In recent years, the development of large language models in the field of artificial intelligence has accelerated rapidly. From GPT-4 to Claude, and from Kimi to DeepSeek-R1, global models are flourishing, with continuous technological upgrades. It is generally believed that the progress of large models is due to the scale of computing power and parameter stacking. However, the core factor determining a model’s ability for “intelligent emergence” is the structure and quality of the data. Large models do not become smarter simply by consuming more data; rather, they become intelligent by consuming structured and high-quality data. Accurately understanding “what kind of data is needed by AI large models” is crucial not only for the upgrade direction of key industrial chains in the new productive forces era but also for national security.

Why Large Models Prefer Structured Data Systems

Current mainstream large models primarily utilize the Transformer architecture, which is designed for natural language processing (NLP) and deep learning tasks. Its attention mechanism does not rely on the literal meanings 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, programming code and mathematical problems are inherently logical, with strict grammar and predictable function organization, allowing the model to learn reasoning paths and planning strategies, thus 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 capabilities, 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 rather a relationship-building process based on “structural recognition.” Without a clear structure, the model cannot extract effective reasoning paths and ultimately relies on statistical simulations, unable to perform true knowledge reasoning and innovation. A clearly defined 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 mainly include five categories, each corresponding to different cognitive abilities of the model.

1. Structured Data: Such as programming code and mathematical logic problems, which form the basis for the model’s reasoning, decision-making, and task planning, supporting its 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: Covering news reports, academic papers, and government disclosure reports. These data are not only authoritative in content and rigorous in language but also coherent in discourse, 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 images with text, audio with text, and video scripts. This type of data develops the model’s representation capabilities in multi-modal spaces, promoting the integration of multi-modal information, which is crucial for building intelligent systems in areas like AI-assisted education, smart healthcare, and industrial automation.

These five types of data are not isolated; they intertwine in application to construct a complex “data network structure.” For example, in smart education scenarios, graphic textbooks (cross-modal) combined 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

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. Firstly, the issue of “structural bias” in data is prominent; for instance, an excessive proportion 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 capabilities. Secondly, the issue of residual bias cannot be overlooked. Data from non-verified sources, such as social media, may contain biases. If used for training without cleaning, the model inherits these biases, potentially leading to inappropriate or erroneous responses in public service scenarios, raising social trust issues. Lastly, there is a scarcity of data in “low-resource areas.” For example, data on minority languages and specific industry standards (such as grassroots medical records and rural governance cases) have not been systematically integrated, limiting 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 the following three aspects:

1. Cognitive-Driven Data Design: Drawing on children’s language acquisition mechanisms, guide the model through a phased approach to master knowledge structures from basic expressions to complex reasoning.
2. Enhancing Data Structure Annotation Capabilities: By incorporating annotations such as causal chains, timelines, and role relationships, help the model establish deeper logical networks, improving its event recognition and judgment capabilities.
3. Exploring AI-Generated Synthetic Data for Training: Under the premise of ensuring data authenticity and validity, utilize the model’s ability to generate well-structured corpora, which can then be reviewed and corrected by professionals, achieving “human-machine co-training” to overcome the bottleneck of insufficient high-quality data.

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

Large models do not solely rely on the traditional approach of “stacking parameters and algorithms” for breakthroughs; rather, they grow as intelligent systems built on “high-quality structured data.” The training and optimization of AI models is a systematic process that requires multi-stage collaborative advancement to achieve continuous performance improvement. Leveraging large-scale unsupervised or self-supervised learning data to conduct tasks like language modeling and image generation enables the model to master basic understanding and generation capabilities. This stage emphasizes the diversity and scale of data; only with sufficiently rich data can the language rules be fully explored and the world’s diverse features presented. Based on pre-training, fine-tuning with accurately annotated data for specific tasks is key to adapting the model to specific application scenarios. The accuracy and consistency of high-quality annotated data determine the model’s precision in tasks like sentiment analysis and object recognition. When authentic annotated data is insufficient, data augmentation and expansion techniques play a crucial role. By means of text paraphrasing, image transformations, or utilizing synthetic data generation, the breadth and depth of the training set can be expanded, enhancing model performance. As times change, new data continuously emerges, 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 indispensable to achieve effective utilization and information integration of cross-modal data.

In the future, the competitive focus of artificial intelligence will not be purely on the scale of model parameters but on who can establish a data system with high structural tension and generalization ability first. This not only relates to a country’s technological strength but also to the initiative and national security at the high ground of scientific and technological development. Industry application models should also transition from “data gatherers” 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 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 chains for national development, requiring both innovative efforts from AI enterprises and guidance and regulation from national policies.