Generate and read: Oh no they didn't
◀ Prev | 2025-05-21, access: Public | Next ▶
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- Link to the paper: https://arxiv.org/abs/2209.10063
prompting text GPT RAG hallucination You can't stop people from asking your language model factual questions, and you can't stop the language model from making up nonsense answers, and that's a problem.
One of the standard solutions is RAG (Retrieval-Augmented Generation): before running the model, you do a search on Wikipedia, and then add the results of the Wikipedia search to the prompt. Then the model only has to explain the Wikipedia articles (which are infallible) in answer to the user's question. Models are much better at summarizing and explaining than at answering questions from their own knowledge, so the hope is that with RAG you're more likely to get answers that are actually true.
The idea in this paper by Yu et al. is to take Wikipedia out of the picture. They run a RAG but instead of using search results, they just run another model (or, quite possibly, just the same model with a specially crafted prompt) to generate pretend Wikipedia articles. And then feed those into the main model the same way one might with real Wikipedia articles. And hope that this concatenation of models with no source of facts will somehow produce answers that are true because, uh...
The damned thing about this, is that it actually seems to work! So the talk explores how that could be.
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