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Main thing is to curate a good set of documents to start with. Garbage in (like Bing google results like this study did) --> Garbage out. From the technical si
by jonathan-adly 3y ago
Main thing is to curate a good set of documents to start with. Garbage in (like Bing google results like this study did) --> Garbage out.
From the technical side, the largest mistake people do is abstracting the process with langchain and the like - and not hyper-optimizing every step with trial and error.
- westurner 3y agoFrom [1], pdfGPT, knowledge_gpt, and paperai are open source. I don't think any are updated for a 10M token context limit (like Gemini) yet either. [1] https://news.ycombinator.com/item?id=39363115 https://news.ycombinator.com/item?id=39363115 From https://news.ycombinator.com/item?id=39492995 https://news.ycombinator.com/item?id=39492995 : > On why code LLMs should be trained on the edges between tests and the code that they test, that could be visualized as [...] "Find tests for this code" "Find citations for this bias" Perhaps this would be best: "Automated Unit Test Improvement Using Large Language Models at Meta" (2024) https://news.ycombinator.com/item?id=39416628 https://news.ycombinator.com/item?id=39416628 From https://news.ycombinator.com/item?id=37463686 https://news.ycombinator.com/item?id=37463686 : > When the next token is a URL, and the URL does not match the preceding anchor text. > Additional layers of these 'LLMs' could read the responses and determine whether their premises are valid and their logic is sound as necessary to support the presented conclusion(s), and then just suggest a different citation URL for the preceding text