3 ms·

Unfortunately this still results in plenty of hallucinations.

• falcor84 2y ago
  What do you mean? Have you or someone else already followed this exact approach?
  • yreg 2y ago
    Supabase did: https://supabase.com/blog/chatgpt-supabase-docs https://supabase.com/blog/chatgpt-supabase-docs I've attempted it as well a year ago (mostly for fun) for our project. Yes, it can still hallucinate. But I would say it's much much much better in this regard than fine-tuning. When I did it, the main issue was that our documentation wasn't exhaustive enough. There are plenty of things that are clear to our users (other teams in the company), but not at all clear to the LLM from the few text excerpts it receives. Also, our context was quite limited back then to just a few paragraphs of text.
  • nestorD 2y ago
    The approach described above is what is commonly referred to as RAG[0]. I am not aware of someone having used it on Wikipedia but, from experience and while it helps, it does not eliminate all hallucinations. [0]: https://en.wikipedia.org/wiki/Large_language_model#:~:text=tool%20use%20is-,Retrieval%20Augmented%20Generation,-%3A%20augment%20an%20LLM https://en.wikipedia.org/wiki/Large_language_model#:~:text=t...