Training LLMs is hard because you need to find good, varied, and fair content in the huge amount of data on the Internet. Whether you're creating a model from scratch or improving an existing one, the quality of the data directly affects how smart the LLM becomes.
This article will explain the data sources and how to use data for LLMs in simple terms, and show how to get data quickly using proxy services.
What is the training data for LLM?
LLM's training data is like a "digital textbook", usually composed of trillions of words of text, from web articles, e-books, papers, codes, etc. We need to clean, segment, and finally convert these data into a format that the model can understand.
It can be understood as removing advertisements and repeated content other than useful information, and breaking long texts into words or phrases . By analyzing the patterns of these data, LLM learns to understand and generate language like humans.
How to train an LLM?
Even non-technical people can understand the training logic of LLM by following the steps below:
1.Collect and clean data: remove useless parts from the large-scale data captured from the entire network
Crawl public content from news websites, encyclopedias, forums, etc. to ensure diversity in topics and language styles .
Perform intelligent cleaning to automatically filter out redundant and low-quality content, and combine manual review to mark sensitive information to improve data purity.
Select the optimal word segmentation strategy based on the characteristics of the target language to improve model training efficiency.
You can use smart proxies to avoid enterprise-level HTTP proxy processing. restrictions and efficiently crawl dynamic web pages. LunaProxy offers free location info at country, state, and city levels. It has a huge pool of over 200 million IPs and covers 195 countries accurately.
2.Training the model: learning in stages
Model selection: Using open-source models like GPT and LLaMA, we can adjust the parameters with our own data to quickly fit business needs. For more professional stuff, we can design a custom model, but that needs a lot of computing power and money.
Training optimization: First, use massive public data to let the model master basic language skills. Then use your own data to teach it specific skills.
3.and correct errors through user feedback
Besides using perplexity and BLEU scores, we also added a manual evaluation to make sure the output content is logical and follows the rules.
Use automated tools to optimize parameters such as learning rate and batch size to reduce manual trial and error costs.
Why does LLM need to integrate public data?
Although synthetic data generated by AI can quickly expand training samples, it has obvious defects:
Lack of authenticity : Synthetic data may contain logical loopholes or fabricated facts that appear plausible but are actually wrong .
Information lag : When new technologies or hot events emerge, they cannot reflect real-world changes in a timely manner.
Lack of diversity : It is easy to repeat existing models and difficult to cover niche areas or cultural differences.
The core value of public data
Authenticity and timeliness
News sites, social media, etc. provide real-time updated content to ensure that the model understands what is really happening in the world right now .
Diverse perspectives and scenarios
Forums contain real conversations among ordinary people, encyclopedias cover professional terms, and e-commerce reviews reflect user needs . This diversity allows the model to adapt to different scenarios.
Correcting bias in AI- synthesized data
Public data can fill in the information blind spots of synthetic data and reduce the model’s incorrect guesses in real recommendations .
Innovative technology for data scraping: How to avoid enterprise-level HTTP proxy processing restrictions?
Traditional crawlers are easily blocked by websites, but modern technology can solve the problem more intelligently:
Agent Services
IP rotation: Combined with IP proxy service, avoid anti-crawling strategies and achieve high concurrent data collection. LunaProxy provides residential proxy services covering 195+ countries, supports SOCKS5/HTTP(s) protocol, and provides flexible pricing plans , with the lowest price plan of $0.77/GB.
Dynamic page crawling: Use a headless browser to simulate user behavior and crawl content dynamically loaded by JavaScript .
Automatic cleaning and sorting
Use AI tools to automatically distinguish "useful information" from "noise" and automatically classify data . Identify cross-platform duplicate content and reduce storage and computing redundancy.
Real-time data stream integration
Access social media APIs to allow the model to learn popular Internet words in real time.
Core public data sources for LLM training
Internet open content
It includes websites in many areas, like tech blogs and industry sites. It also has search results from Google and Bing, and product details and reviews from Amazon. This gives the model language samples from different situations.
Public library resources
The online book platform offers classic books that are out of copyright. These books cover literature, philosophy, history, and more. They help the model learn how to write well and handle long texts.
Social and interactive platform
Places like Reddit and Stack Overflow have real talks, expert questions and answers, and industry chats. They help get everyday expressions and knowledge from specific fields.
Scientific research library
arXiv and PubMed have lots of papers and reports. They help the model learn scientific words, data thinking, and professional writing.
News Information Platform
Google News and BBC give up-to-date reports. They help the model learn about current events, politics, economy, and how to write news in different languages.
Open Encyclopedia
Wikipedia can have mistakes because anyone can edit it. But its articles in many languages and organized knowledge are still useful for training general LLMs.
Developer Ecosystem Content
GitHub and Kaggle have open-source code, tech docs, and coding talks. They help models get better at coding rules, algorithm thinking, and real-world engineering.
Video and multimedia text
YouTube's automatically generated subtitles, podcast transcriptions, etc. provide learning materials for spoken dialogues and cross-modal associations (text-video), enhancing the model's situational adaptability.
Practical advice
Low-cost start: Prioritize the use of open source datasets and use Lunaproxy to capture supplementary data.
Compliance first: Avoid collecting user privacy and comply with platform rules (such as the Robots protocol).
Continuous updating: Regularly crawl the latest content to prevent the model from becoming "outdated".
By mixing public data with their own data and using smart proxy tech, companies can train better LLMs for less money and really unblock AI's business power!Log in to LunaProxy now to enjoy great proxy services.
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