Row 98132

Row ID: 98132 | Dataset Entry | Axioma AXP Content Repository

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I work in physics and computer science and use gpt constantly across all fields.

Did you check it gave the correct answer? In my experience, either you are over estimating the difficulty of what you asked, or it didn't actually give the correct answer but looked like it did. There are a lot of technical reasons underlying this, but basically it is almost incapable of doing what you said UNLESS either a) the numbers you used were very carefully picked(in ways you may not have noticed - selecting standard filler values from a textbook for example,), or b) it wrote a computer program to do it and/or used a calculator for the calculations AND it required no algebraic manipulation.

The B case is actually borked as well, but you can stretch its abilities a fair but further.

Either way, LLMs are much worse at math then they first appear. This is getting patched a bit with clever plugin use, but I wouldn't even vaguely trust a bridge built by gpt.

Btw, this is not a "more training" thing, it's a fundamental limit of the current approach, and why people were so excited about Q* last year. IF/WHEN it can do this, it will be a big deal, but it can't do it reliably at the moment.

FieldValue
text I work in physics and computer science and use gpt constantly across all fields. Did you check it gave the correct answer? In my experience, either you are over estimating the difficulty of what you asked, or it didn't actually give the correct answer but looked like it did. There are a lot of technical reasons underlying this, but basically it is almost incapable of doing what you said UNLESS either a) the numbers you used were very carefully picked(in ways you may not have noticed - selectin…
label r/chatgpt
dataType comment
communityName r/ChatGPT
datetime 2024-05-25
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  "text": "I work in physics and computer science and use gpt constantly across all fields.\n\n\nDid you check it gave the correct answer? In my experience, either you are over estimating the difficulty of what you asked, or it didn't actually give the correct answer but looked like it did. There are a lot of technical reasons underlying this, but basically it is almost incapable of doing what you said UNLESS either a) the numbers you used were very carefully picked(in ways you may not have noticed - selecting standard filler values from a textbook for example,), or b) it wrote a computer program to do it and/or used a calculator for the calculations AND it required no algebraic manipulation.\n\n\nThe B case is actually borked as well, but you can stretch its abilities a fair but further.\n\n\nEither way, LLMs are much worse at math then they first appear. This is getting patched a bit with clever plugin use, but I wouldn't even vaguely trust a bridge built by gpt. \n\n\nBtw, this is not a \"more training\" thing, it's a fundamental limit of the current approach, and why people were so excited about Q* last year. IF/WHEN it can do this, it will be a big deal, but it can't do it reliably at the moment.",
  "label": "r/chatgpt",
  "dataType": "comment",
  "communityName": "r/ChatGPT",
  "datetime": "2024-05-25",
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