Can LLMs Reason and Plan?
cacm.acm.org
Can LLMs Reason and Plan?
1–10 of 51 posts
Re: Can LLMs Reason and Plan?
#2These things are just statistical language models, aren't they? To the extent that when people reason or plan and then verbalize those plans, they tend to leave certain trajectories through language space, a statistical model could presumably reproduce similar, plausible sounding narratives. Doesn't mean that any kind of agency ever actually thought about those plans though, in the way that a person would.
Re: Can LLMs Reason and Plan?
#3LLMs can not plan. There is no LLM that can solve sudoku puzzles by executing the obvious constraint propagation algorithm with backtracking. Therefore, LLMs can neither reason nor plan. Software is not magic and the fact that a lot of people are starting to think that it is should be concerning for the folks training the next generation of software engineers.
Re: Can LLMs Reason and Plan?
#4LLMs can not plan. There is no LLM that can solve sudoku puzzles by executing the obvious constraint propagation algorithm with backtracking. Therefore, LLMs can neither reason nor plan. Software is not magic and the fact that a lot of people are starting to think that it is should be concerning for the folks training the next generation of software engineers.
I would hope the next generation of softwares engineers are not the kind to blindly buy into media hype.
Re: Can LLMs Reason and Plan?
#5No.
Re: Can LLMs Reason and Plan?
#6LLMs can not plan. There is no LLM that can solve sudoku puzzles by executing the obvious constraint propagation algorithm with backtracking. Therefore, LLMs can neither reason nor plan. Software is not magic and the fact that a lot of people are starting to think that it is should be concerning for the folks training the next generation of software engineers.
I would hope the next generation of softwares engineers are not the kind to blindly buy into media hype.
The next generation (going by some comments I’ve read on HN) are already non-programmers gluing (I assume terrible) LLM-generated code together until it (appears like it) works.
Hell, my non-programmer brother recently sent me a message like “can you think of a way to fix this script, ChatGPT isn’t managing” (sends me a badly written AI-generated script)
Re: Can LLMs Reason and Plan?
#7[deleted]
Re: Can LLMs Reason and Plan?
#8The article itself is very assertive and makes a lot of generalizations, but if you look at the source of their claims [1] you see that in the first study they are using GPT-3.5 and achieve only a 5% score on a reasoning test that largely relies on spatial intuition - some boxes need to be stacked and unstacked sequentially in a convoluted task. Then, they get criticised so they come up with another paper in which they use GPT-4, which has an improved performance of 30% - a 6-fold increase, which the author describes as "modest". They then decide to change the test to a much harder and more convoluted version, where (surprise!) performance drops down once again [2].
I would also have welcomed a comparison to humans. If we apply this test to 100 humans, can we conclude humans don't reason if only 30 get it right?
Re: Can LLMs Reason and Plan?
#9These things are just statistical language models, aren't they? To the extent that when people reason or plan and then verbalize those plans, they tend to leave certain trajectories through language space, a statistical model could presumably reproduce similar, plausible sounding narratives. Doesn't mean that any kind of agency ever actually thought about those plans though, in the way that a person would.
"plausible sounding" is probably the best two word summary of LLM output. A good one word summary might be "bullshit".
Re: Can LLMs Reason and Plan?
#10The article itself is very assertive and makes a lot of generalizations, but if you look at the source of their claims [1] you see that in the first study they are using GPT-3.5 and achieve only a 5% score on a reasoning test that largely relies on spatial intuition - some boxes need to be stacked and unstacked sequentially in a convoluted task. Then, they get criticised so they come up with another paper in which th…
Interesting take. I feel in this discussion, many people are approaching it from the theoretical limitations of LLMs, and you seem the only one taking the experimental approach. Funny enough, many who doubted LLMs capabilities 4 years ago, have come around their emergent capabilities, yet with much skepticism, simply because we still don't understand how these emergent abilties work. I haven't seen a paper comparing the threshold of performance with the LLMs increased capabilities, and what parameter (and their weights) come into play to influence the performance.