Ya I don’t think I’ve seen any article going in depth into just how many low level humans like data labelers and RLHF’ers there are behind the scenes of these big models. It has to be millions of people worldwide.
https://www.theverge.com/features/23764584/ai-artificial-int...
Interestingly, despite the boring and rote nature of this work, it can also become quite complicated as well. The author signed up to do data labeling and was given 43 pages (!) of instructions for an image labeling task with a long list of dos and don'ts. Specialist annotation, e.g. chatbot training by a subject matter expert, is a growing field that apparently pays as much as $50 an hour.
"Put another way, ChatGPT seems so human because it was trained by an AI that was mimicking humans who were rating an AI that was mimicking humans who were pretending to be a better version of an AI that was trained on human writing..."
Right now there are top tier LLMs being produced by a bunch of different organizations: OpenAI and Anthropic and Google and Meta and DeepSeek and Qwen and Mistral and xAI and several others as well.
Are they all employing separate armies of labelers? Are they ripping off each other's output to avoid that expense? Or is there some other, less labor intensive mechanisms that they've started to use?
I mean on LinkenIn you can find many AI trainer companies and see they hire for every subject, language, and programming language across several expertise levels. They provide the laborers for the model companies.
[0]: https://snorkel.ai/data-labeling/#Data-labeling-in-the-age-o...
[1]: https://cdn.openai.com/papers/Training_language_models_to_fo...
[2]: https://www.businessinsider.com/chatgpt-openai-contractor-la...
https://www.theverge.com/features/23764584/ai-artificial-int...