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Notes -
Bad at: reading simple, easily available sports tables, apparently? I asked it to provide the team name and points tally of a few teams that have finished in a 4th position or higher in the league table while having few points, in a defined range of seasons. It couldn't do it, fabricating something every time. After being corrected several times, it still went back to fabrication when asked again.
That's one bug that I think will be ironed out fairly quickly and easily. LLMs aren't really great at storing information in a lossless way. But in the very near future they will have access, via plugins, to lossless stores of truth.
Surely, GPT-4 could come up with the correct SQL for querying a database of sports results to give you the result you want.
So it just needs to have access to the data in a SQL table that it can query.
You can already cook something similar up using langchain and a vector db. It doesnt need to be baked into the model architecture!!!
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It's actually good at dealing with tables even in its current rudimentary variant. I copied a table of math formulas out of a PDF and asked GPT to describe them and despite looking to me like a complete pile of jumbled garbage when I CTRL-V'd them into the prompt box, it transcribed them perfectly. One thing I want to try is converting pictures to ASCII and seeing whether it can comment on them, although I believe people have already shown it can do so pretty well.
Yeah, I don't know why it craps out on sports league tables.
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Would you mind elaborating on that? Where do lossless stores of truth exist right now? How do the plugins create or use them?
Here's a salient example: https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its-wolfram-superpowers/
By "lossless" I mean there is no compression loss.
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Simple example: when it detects that you want some numbers added, it queries a calculator plugin instead of trying to ape addition through its own text generation.
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