Apple study proves LLM-based AI models are flawed because they cannot reason
1–10 of 24 posts
Re: Apple study proves LLM-based AI models are flawed because they cannot reason
#2This is an interesting acid test:
- article is titled "GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models"
- some people having been flogging it as "LLMs cannot reason"
- it shows a 6-8 point drop, in test results in the 80s, if you replace the #s in the test set problems with random #s, and run multiple times
- If anything, sounds like a huge W to me: very hard to claim they're just memorizing with that small of a drop
Re: Apple study proves LLM-based AI models are flawed because they cannot reason
#3What an amazing time we live in when people are discussing how perhaps the output from a bunch of matrix convolutions possibility can't quite reason about mathematics.
Re: Apple study proves LLM-based AI models are flawed because they cannot reason
#4This is silly. Humans will also get fewer right answers if you make the question more complex (requiring additional steps), or if you add irrelevant information as a distraction (since on tests, there's usually an assumption that all information given is relevant). As for changing names and numbers, the large effects they saw were all on small (<10B param) open source models; the effects on o1 were tiny and barely distinguishable from noise.
Re: Apple study proves LLM-based AI models are flawed because they cannot reason
#5Gosh I wish someone would pay me handsomely for coming up with such stupidly obvious "research" results as "a computer program that uses statistics to pick the next word in a sequence doesn't reason like a person".
Re: Apple study proves LLM-based AI models are flawed because they cannot reason
#6Interesting. I would have thought that the training set (basically the whole internet AIUI) would have included various "teacher's version" exams with enough word problems with intentionally-distracting extra information, that the models would be able to ignore that sort of thing.
This sounds like they're inspecting existing models. Maybe a model trained specifically on "word problem" question-answer pairs (as in, the sort of things that show up on tests and always pretend that the sort of complications a domain expert would know about just don't exist) would do better?
Re: Apple study proves LLM-based AI models are flawed because they cannot reason
#7They tested o1-preview but the results hidden away in the Appendix, probably because o1-preview's "drops" for 4 out of 5 of the new benchmarks are all within the margin of error.
i.e 94.9 on the full GSM8K and 93.6, 92.7, 95.4, 94 and 77.4 on the modified benchmarks.
The study proves nothing of the sort. Even the results of 4o are enough to give pause to this conclusion.
Re: Apple study proves LLM-based AI models are flawed because they cannot reason
#8[deleted]
Re: Apple study proves LLM-based AI models are flawed because they cannot reason
#9Interesting. I would have thought that the training set (basically the whole internet AIUI) would have included various "teacher's version" exams with enough word problems with intentionally-distracting extra information, that the models would be able to ignore that sort of thing. This sounds like they're inspecting existing models. Maybe a model trained specifically on "word problem" question-answer pairs (as in, th…
They hid away the results of o1 preview in the Appendix but it does not drop below margin of error numbers on 4/5 of their modified benchmarks. The last one they add "seemingly relevant but ultimately irrelevant information to problems" and it drops to 77%. Now I'm willing to bet this is within human baselines but either way, researchers really need to start including human baselines in these kinds of papers.
Re: Apple study proves LLM-based AI models are flawed because they cannot reason
#10Gosh I wish someone would pay me handsomely for coming up with such stupidly obvious "research" results as "a computer program that uses statistics to pick the next word in a sequence doesn't reason like a person".
AlphaProof was able to get a silver IMO medal by writing formally verified proofs of novel mathematical problems:
https://deepmind.google/discover/blog/ai-solves-imo-problems...
Whether this is "like a person" or not, it seems silly to insist that this "doesn't count" as mathematical reasoning. I certainly couldn't get an IMO silver medal and I have a degree in math.