4 ms·
The distinction here is between 'the /kind/ of data you're throwing at it' and 'data you have already thrown at it.' The result shows that the LLM has a hard t
by sdenton4 2y ago
The distinction here is between 'the /kind/ of data you're throwing at it' and 'data you have already thrown at it.'
The result shows that the LLM has a hard time generalizing to new (basic!) problems that were not in its training set; this suggests it is memorizing solutions rather than understanding the mechanisms... It's the undergrad who has crammed Leetcode solutions for two weeks before their interview, rather than a student with a deeper understanding who can successfully address new problems.
Having a friend who is objectively pretty dumb but has memorized the entire existing literature of your field of study is still pretty damned useful, however. You just need to understand what kinds of questions you can trust them with.
Along these lines, my own best use of LLMs is looking up jargon and old results. I see some complicated problem, propose a method for attacking it, and ask my friend who has read every stats paper ever digitized whether they have seen this kind of analysis before, what it's called, why it's a bad idea, and what people do instead. The answers point me to relevant literature by giving me the right jargon and method names to search for using Good-Old-Fashioned-Google.
- qsort 2y agoIn addition to that -- maybe it's just my tinfoil hat speaking -- I think it calls into question whether benchmark numbers are really that meaningful. "The machine has a 90% success rate when we tell the machine the answer" is a bit weaker than how those numbers were presented.
- OtherShrezzing 2y agoI think it's becoming widely accepted that the current benchmarks aren't especially useful metrics for intelligence - rather, they're useful metrics for measuring how well a system can answer the benchmark questions.
- sdenton4 2y agoJust like the SATs!
- conception 2y agoSummarizing: LLMs are autocomplete, not thinking machines.
- sdenton4 2y agoThis is still too reductive, IMO. The kinda dumb colleague who has memorized all the literature is not just auto-complete. When I ask about some statistical technique that I didn't know the name of, being able to connect my description to the correct methods in the literature isn't 'just auto-complete.' They just have a particular set of strengths and weaknesses, same as any tool. Figuring out their strengths and limitations is how you use them well. And dismissing them for not fully solving AGI is short-sighted.
- hilbertseries 2y agoThe gpt4 model was able to draw a unicorn and given a program that drew a unicorn, modify it so the unicorn was rotated 90 degrees. https://arxiv.org/abs/2303.12712 https://arxiv.org/abs/2303.12712
- ryandv 2y ago> The result shows that the LLM has a hard time generalizing to new (basic!) problems that were not in its training set; this suggests it is memorizing solutions rather than understanding the mechanisms [...] I am once again reminded of Searle's "Chinese room" thought experiment [0] in which it is argued that computers cannot understand Chinese, merely memorize and regurgitate a canned set of responses to given prompts, and execute procedures by rote; that their computations cannot be "about" a subject matter (such as Chinese, or programming) the same way the operation and contents of our minds are "about" something. [0] https://plato.stanford.edu/entries/chinese-room/ https://plato.stanford.edu/entries/chinese-room/