Training a large language model isn’t about building a bigger neural network-it’s about feeding it the right data. If you think GPT-4 or Llama 3 just swallowed the entire internet and spat out answers, you’re missing the real story. The truth is, less than 15% of the raw web data collected ever makes it into the training set. The rest? Deleted, filtered, or rewritten. And that’s where the real work happens.
Where the Data Comes From
Most large language models start with Common Crawl, a non-profit archive that scrapes billions of web pages every month. It’s free, massive, and messy. By 2025, Common Crawl had processed over 25 billion pages, totaling more than 200 terabytes of raw HTML, JavaScript, and embedded text. But that’s just the starting point. You don’t train a model on raw HTML-you train it on clean, readable text. And even then, you’re not done. Other sources include Wikipedia, books from Project Gutenberg, GitHub code repositories, scientific papers from arXiv, and curated datasets like RefinedWeb. Some companies, like Apple and Meta, also license proprietary text from legal agreements with publishers or educational institutions. But the bulk? Still the open web.Why Cleaning Matters More Than You Think
You might assume more data equals better performance. But Apple’s 2024 BETR research proved the opposite. Their team found that training on cleaned, targeted data improved model performance by up to 2.1 times compared to using unfiltered web data. That’s not a small gain-it’s a multiplier. In fact, for models with over 70 billion parameters, overly aggressive filtering hurt performance. The sweet spot? Retaining about 30-40% of the original data. Too little, and the model lacks depth. Too much, and it learns noise. One major issue? Duplicates. If the same paragraph appears 500 times across forums, blogs, and comment sections, the model doesn’t learn better-it learns to memorize. This is called the “double descent” effect. The model starts copying instead of reasoning. That’s why deduplication isn’t optional-it’s the first rule of data cleaning.How Deduplication Works at Scale
You can’t manually delete duplicates from a 13-trillion-token dataset. So you use algorithms. The most common method is simhash. It turns each document into a 64-bit fingerprint. If two documents have fingerprints that match within a few bits, they’re likely duplicates. One engineer on Reddit reported cutting deduplication time from 14 days to just 9 hours on a 50TB corpus using this method. But here’s the catch: document-level deduplication isn’t enough. A 2024 study on the Dolma dataset showed that paragraph-level deduplication improved downstream task performance by 7.3%. Why? Because a single article might have 10 unique paragraphs and 20 copied ones. If you delete the whole document, you lose the good parts. So modern pipelines check every paragraph individually-even if it means tripling the processing time.
Quality Filtering: The Gatekeepers
Not all text is created equal. A blog post about “best pizza in Phoenix” might be perfectly readable. But a forum thread with 200 spam replies? Or a scraped product page with 80% JavaScript garbage? Those get tossed. Modern pipelines use a tiered approach. First, lightweight models scan for basic quality signals: sentence length, punctuation density, language confidence, and HTML tag ratios. If a document scores below a threshold, it’s filtered out. This removes about 40-60% of the raw data. Then comes the heavy lifting. Advanced LLMs, trained specifically for quality scoring, evaluate the remaining text. They look for coherence, factual consistency, and logical flow. A 2024 NVIDIA paper showed that using a smaller LLM as a “filtering judge” was 3x faster than training a full-sized model from scratch-and just as accurate.Toxicity, Copyright, and Legal Risks
This is where things get messy. You can’t train a model on hate speech, doxxing, or pirated books. But how do you define “toxic” without over-filtering? A 2024 survey of 127 ML engineers found that 68% considered removing toxic content their biggest challenge. In medical and legal domains, false positives hit 18-22%. A sentence like “The patient was diagnosed with depression” might get flagged as “mental health risk content.” A legal quote from a court ruling? Flagged as “copyrighted material.” Copyright is another nightmare. A 2024 analysis by Fenwick & West estimated that 15-25% of training data might need reprocessing due to pending lawsuits. Companies now spend 35-40% of their pipeline resources on copyright filtering-even though it often adds less than 1% to model performance. The EU AI Act, effective February 2025, made this worse. Now you need to log every source, timestamp, and license type. That’s another 20-30% overhead.Synthetic Data: The New Wild Card
What if you could generate your own high-quality training data? That’s where synthetic data comes in. DeepSeek-R1 used reinforcement learning to create thousands of math problem-solving chains-then used rejection sampling to keep only the ones that were logically sound. The result? A model that outperformed others on arithmetic benchmarks, even though it was trained on far less real-world data. Synthetic data is especially useful for rare domains: quantum physics, rare medical conditions, or legal precedents from small countries. But it’s risky. If the generator learns to fake patterns instead of understanding them, the model becomes brittle. A 2024 Turing Labs study found that 31% of synthetic datasets introduced subtle logical errors that only showed up after deployment.
Resource Costs and Pipeline Timelines
Building a web-scale data pipeline isn’t cheap. It takes 3-6 months to design, test, and deploy. You need:- 50-100 dedicated crawling nodes to handle billions of pages
- Thousands of GPU hours for filtering and deduplication
- Specialized engineers in distributed systems (Spark, Flink), NLP, and cloud infrastructure
The Future: Targeted Pretraining
The era of “dump everything and hope it works” is over. The next big shift is targeted pretraining. Instead of training on the entire web, you train on data that mirrors your end task. Apple’s BETR method selects documents based on how similar they are to benchmark questions. If your model needs to answer medical questions, you prioritize medical papers, clinical notes, and health forums-even if they’re rare on the web. Gartner predicts that by 2027, 80% of enterprise LLMs will use task-specific corpora instead of general web data. This isn’t just about performance. It’s about ethics, cost, and control. Why train on millions of spammy Reddit threads when you can curate 100,000 high-quality legal briefs? The data is smaller, cleaner, and legally safer.What You Should Do Now
If you’re building a custom LLM:- Start with a small, clean dataset-50GB max. Test your filtering pipeline on it first.
- Use simhash for paragraph-level deduplication. Don’t skip this.
- Don’t filter for toxicity blindly. Use human review on a sample before automating.
- Track your retention rate. If you’re keeping more than 30% of raw data, you’re probably not filtering enough. If you’re keeping less than 10%, you’re over-filtering.
- Consider synthetic data for niche tasks. But validate every generated example with real-world benchmarks.
How much data is needed to train a large language model?
State-of-the-art models like GPT-4 are trained on approximately 13 trillion tokens of text. But that’s after cleaning. Raw data collection starts at 200-500 terabytes, and after filtering, only 10-25% remains. For smaller, domain-specific models, 50-200GB of clean data is often sufficient.
What’s the biggest mistake people make in data cleaning?
Over-relying on automated filters without human validation. Toxicity detectors, copyright scanners, and quality models all produce false positives. One team removed 12% of medical text because it mentioned “suicide,” even though it was from legitimate clinical notes. Always sample and review what gets filtered.
Can I use Common Crawl without legal issues?
Technically, yes-but you’re not off the hook. Common Crawl doesn’t guarantee copyright compliance. If you use it for commercial models, you still need to filter out copyrighted content. The EU AI Act now requires you to document your data sources and filtering steps. Ignoring this risks regulatory penalties.
Is synthetic data better than real data?
Not always. Synthetic data excels in niche domains where real data is scarce-like advanced math or rare medical cases. But for general language understanding, real web text still outperforms. The best approach is hybrid: use real data for broad knowledge, synthetic for precision tasks.
How long does data cleaning take compared to model training?
On average, data cleaning takes 2-3 times longer than training the actual model. For a 70B-parameter model that trains in 3 weeks, expect 6-9 weeks of data prep. Some teams spend months just on deduplication and legal filtering. Data is now the bottleneck-not compute.
