Against the backdrop of the rapid development of artificial intelligence, large language models (LLMs) have become an important engine for promoting natural language understanding, automatic generation, and multimodal interaction. However, building a powerful and knowledge-rich large language model requires large-scale, high-quality training data. In this process, a technical support that is often overlooked but crucial is proxy technology.
- Data is the “fuel” of large models
Whether it is OpenAI’s GPT, Anthropic’s Claude, or locally deployed large models such as LLaMA and Mistral, they all rely on multi-source heterogeneous data for pre-training. These data usually come from:
Public web pages (such as news, forums, encyclopedias, etc.)
Open source datasets (such as C4, The Pile, Common Crawl)
Technical documents and codes (such as GitHub, Stack Overflow)
Domain corpus (such as financial, legal, medical documents)
User conversations, comments and social media content
However, many high-quality data are not provided in packages and can only be collected dynamically through web crawling. In order to prevent abuse, modern websites often set up strict anti-crawling mechanisms, such as: IP blocking, frequency restrictions, User-Agent detection, verification code challenges, etc.
At this time, the importance of proxy technology is highlighted.
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How does the proxy support large model data collection
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Hide the real identity and break through the anti-crawling restrictions
Residential proxies (Residential Proxy) or mobile proxies use the IP address of the real user network to make the crawler look more like an ordinary user, greatly reducing the risk of being blocked. Compared with data center proxies, residential IP distribution is more natural and more difficult to identify.
For example, the SOCKS5 residential proxy service provided by PiaProxy has a dynamic IP pool of more than 350 million, covering multiple countries and regions around the world, suitable for large-scale concurrent access.
- High-concurrency crawling to improve data collection efficiency
By rotating IPs in the proxy pool, multi-threaded distributed crawling can be achieved to avoid triggering access frequency restrictions. With the AI scheduling system, proxy traffic can be dynamically managed, regions can be allocated, and success rates can be monitored to ensure stable data acquisition.
- Support directional collection to improve corpus quality
Some proxy services support geographic location selection and operator designation, which are suitable for collecting regional specific corpora, such as: collecting English education websites, Arabic legal documents, Japanese medical forums, etc. Precision collection can be used to build multi-language and multi-domain models.
- Combine AI for intelligent anti-crawling and behavior simulation
High-level systems will introduce AI models to simulate user behaviors (such as clicks, scrolling, dwell time, etc.), and with proxies, they will further break through the protection wall and achieve “human-like” web page interaction, further improving the concealment and quality of data collection.
- Real challenges in building data pipelines
Although proxy technology solves the IP blocking problem, building high-quality data pipelines still faces many challenges:
Data legality and compliance requirements (GDPR, privacy terms, etc.)
Data governance processes such as deduplication, cleaning, and format unification
Analysis and adaptation of different page structures
Traffic cost, proxy service fee and resource scheduling balance
Therefore, in actual projects, it is usually necessary to combine proxy, distributed crawling, content filtering, semantic annotation, metadata management and other modules to build a complete data platform.
- Conclusion
The capability boundary of a large language model depends on its data boundary. Before building a model, a powerful and sustainable data acquisition system must be built first. In this system, proxy technology, especially residential proxy and intelligent proxy scheduling systems, is becoming the underlying driving force for data acquisition.
It can be said that without a powerful proxy system, there is no “knowledge base” required for large models. As AI models move towards wider scenarios, more languages and professional fields, proxy technology will also become an indispensable part of data infrastructure.