> LLMs do not do this, and if you're training the LLMs (at cost) to do this, you're already having to do the very same searching out of materials within the cor

> LLMs do not do this, and if you're training the LLMs (at cost) to do this, you're already having to do the very same searching out of materials within the corpus related to what you want. A more reasonable suggestion would be not training a LLM (which one doesn't want to do anyway) but treating it as a retrieval+summarization task: search the corpus for mentions and similar-by-embedding documents, and summarize. LLMs are good at abstractive summarization with minimal hallucination or error. This can serve as an 'annotated bibliography', a first pass for a human writing it themselves, or the collective summaries be fed into the LLM for a summary. The main problem here is I guess that most of the relevant texts have poor or no OCR, so one can't do that in the first place. But there's a good chance that that will mostly stop being an issue in a few years as 'text' LLMs move to images (see eg PIXEL https://arxiv.org/abs/2207.06991 https://arxiv.org/abs/2207.06991 or Kosmos https://arxiv.org/abs/2302.14045 https://arxiv.org/abs/2302.14045 or https://arxiv.org/abs/2010.10648#google https://arxiv.org/abs/2010.10648#google https://arxiv.org/abs/2012.14271 https://arxiv.org/abs/2012.14271 https://arxiv.org/abs/2209.14156 https://arxiv.org/abs/2209.14156 ) and they will either OCR, embed, or just process images of complex text directly. So, something to keep an eye on, perhaps: there's never going to be enough humans to do all this archiving properly, but perhaps there may eventually be enough GPUs to do it...