Prompt

I know that LLMs are trained on what are commonly described as vast chunks of random internet text. But the details of this process often seem shrouded in mystery. What exactly ARE these random repositories of general information? Who gathers them? How? And why? Were people gathering up these general internet repos before LLMs became popular? If so, why? Besides well known examples like the internet archive, how long have random organisations been gathering up these vast chunks of data? What kind of oversight exists over these orgs? Who are they? What motivates them to do this? What kind of storage is required? Speaking of which, how do they afford the vast compute to the kind of scraping that is presumably required to amass these data stores? "Big tech" is commonly cited by privacy advocates as the bogeyman of data privacy .. should we be worrying about these random data gobblers instead? And finally ... I understand that some of these crawls are open source. I found one archive that was about 10TB. I can download that onto my NAS. If I do so, will I just find a bunch of random internet content? Please address my questions one by one in a detailed document!

Output

Large Language Models (LLMs) are trained using vast amounts of text data sourced from various repositories on the internet. Here's a detailed look at how these data repositories are gathered, who is involved, and what implications they have:

What Are These Random Repositories of General Information?

The data used to train LLMs comes from a variety of sources, including websites, online books, research papers, code repositories, and more. These are often publicly available datasets that are scraped and collected to form large corpora of text data[5][8]. Examples include Common Crawl, Wikipedia, and other open-source datasets like The Pile and C4[5].

Who Gathers Them and How?

Data is gathered by organizations and companies specializing in web scraping and data collection. This process involves using automated tools to extract data from websites and other online sources. Companies like Apify provide storage solutions optimized for the large-scale data scraping required for LLM training[2]. Web scraping can be done in-house by companies or outsourced to specialized agencies or freelancers[1].

Why Are They Gathered?

Initially, data collection was driven by various needs such as market research, academic research, and archiving purposes. With the rise of AI and machine learning, particularly LLMs, the demand for large datasets has increased significantly. These datasets help train models to understand language patterns, context, grammar, and meaning across different domains[6][8].

Historical Context

Organizations have been gathering large datasets long before the popularity of LLMs for purposes like archiving (e.g., Internet Archive) or research. However, the scale and purpose have evolved with advancements in AI technologies.

Oversight and Regulation

Oversight varies by region and is often limited. In some cases, there are privacy laws such as GDPR in Europe or CCPA in California that regulate data collection practices to some extent[4]. However, many organizations operate with minimal oversight unless they fall under specific legal jurisdictions or sectors that require compliance with privacy regulations[3][9].

Motivations Behind Data Collection

The motivations include training AI models, conducting market research, enhancing search engines, and more. For commercial entities, these datasets can be monetized through improved services or products.

Storage Requirements

Storing such vast amounts of data requires scalable cloud storage solutions capable of handling terabytes or even petabytes of information. Companies often rely on cloud services like Amazon Web Services for this purpose[2].

Cost of Data Collection

Web scraping can be costly due to the need for technical expertise, server resources, and IP proxies. Costs can range significantly depending on the complexity of the scraping task and the volume of data collected[1].

Privacy Concerns

While "big tech" companies often face scrutiny over data privacy issues[7], smaller organizations collecting data also pose privacy risks if they do not adhere to data protection laws. The lack of transparency in how some organizations collect and use data can be concerning.

Open Source Data Crawls

Some web crawls are open-source and available for download. For example, Common Crawl offers datasets that can be several terabytes in size. If you download such a dataset onto your NAS, you will likely find a mix of random internet content formatted in a way suitable for machine learning tasks[8].

In summary, while LLMs rely on vast datasets gathered from various internet sources, the process involves multiple stakeholders with varying degrees of oversight. The motivations range from commercial to research purposes, with significant implications for privacy and data security.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.