Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Simple tasks showing reasoning breakdown in state-of-the-art LLMs

arxiv.org

Post reply on HN
Print view View original

271–280 of 393 posts

Re: Simple tasks showing reasoning breakdown in state-of-the-art LLMs

#271

Post by gwbas1c »

emmender2 wrote: post #73
gwbas1c wrote: post #49

Earlier quoted context omitted.

> an entire paper written on one prompt - is this the state of research these days Years ago I attended an AI meetup where the organizer would discuss the differences between reductionist and wholistic research. This is reductionist research. In this case, reducing the paper to one prompt is to allow for objective comparison of models. Otherwise, without a reductionist approach, how does one perform such an experimen…

what I would like to see is a parameterized class of prompts which can never be solved by the LLMs even when a finite number of them are manually added to the dataset.

Well, should we consider ChatGPT a Turing machine? Otherwise, I think an answer like that either requires significantly more research; insight; or general knowledge about how LLMs work.

IE, you're getting into areas that are analogous to Turing's theories. I don't think he came up with those theories overnight.

Re: Simple tasks showing reasoning breakdown in state-of-the-art LLMs

#272

Post by bryan0 »

Like others I ran this through gpt-4o and it fails at reasoning because it does not realize Alice is a sister. In fact in one explanation it defines the siblings as: M brothers, N sisters, 1 Alice (!!)

I asked gpt “Is Alice a sister?” It answered yes and then answered the problem correctly

Re: Simple tasks showing reasoning breakdown in state-of-the-art LLMs

#273

Post by Workaccount2 »

photon_lines wrote: post #266
Workaccount2 wrote: post #259

Earlier quoted context omitted.

We have no idea how human thinking works and no idea what consciousness is. I'm getting so tired of listening to software engineers LARP pseudo neuroscientists with 6th grade level insights. >Of course, the [AI] brain isn’t ‘conscious.’ It doesn’t have any survival instincts which we humans do. Bruh...

So instead of shouting insults, maybe you can shine some light on how anything stated there is wrong? Everything that I said there is true - we don't have a good mapping of what consciousness is - if you want to add some insights add them instead of making insulting and useless statements on HN.

We have no idea if ChatGPT is conscious or not. We have no idea if it is "thinking" or not.

What we know with a reasonably high level of certainty is that consciousness and "thought" are physical processes. That's about it.

Pulling out the scalpel to start dividing up what physical process is and isn't conscious is a fools errand. And especially foolish when just making up arbitrary qualifications for it.

Am I saying that ChatGPT is conscious? No. But I am saying is you shouldn't give much credence to people who are anything more than agnostic about it.

Re: Simple tasks showing reasoning breakdown in state-of-the-art LLMs

#274

Post by viking123 »

yoyohello13 wrote: post #106
GPerson wrote: post #10

Earlier quoted context omitted.

There must be a name for the new phenomenon, of which your post is an example, of: 1. Someone expresses that an LLM cannot do some trivial task. 2. Another person declares that they cannot do the task, thereby defending the legitimacy of the LLM. As a side note, I cannot believe that the average person who can navigate to a chatgpt prompter would fail to correctly answer this question given sufficient motivation to d…

Many people, especially on this site, really want LLMs to be everything the hype train says and more. Some have literally staked their future on it so they get defensive when people bring up that maybe LLMs aren’t a replacement for human cognition. The number of times I’ve heard “but did you try model X” or “humans hallucinate too” or “but LLMs don’t get sleep or get sick” is hilarious.

The comment that humans are sometimes wrong too is absolutely ridiculous and I always think about the self-driving car incident in china where the car mistook an advertisement of a car above the road as a real car and hit the breaks causing a chain crash. Even the shittiest driver would never do that.

Re: Simple tasks showing reasoning breakdown in state-of-the-art LLMs

#275

Post by viking123 »

thinkr42 wrote: post #169

Genuine curiosity here and trying to temper my own selective memory : does it seem like skepticism is growing on the AI front in the news recently?

At least in my tech circle, most people understand the limitations and treat is as a good tool (which it is imo). In many cases it's better than google if you are even somewhat familiar with the domain and can spot the occasional bullshit. No clue what the average people think, some are convinced singularity will come in 2 weeks but most probably have no idea

Re: Simple tasks showing reasoning breakdown in state-of-the-art LLMs

#276

Post by IanCal »

photon_lines wrote: post #44
nerdjon wrote: post #18

For anyone considering reading the paper and like me don't normally read papers like this, open the PDF and think you don't have time to read it due to its length. The main part of the paper is the first 10 pages and a fairly quick read. On to the topic here. This is an interesting example that they are using. It is fairly simplistic to understand as a human (even if we may be inclined to quickly jump to the wrong co…

There's actually a pretty simple solution to this that I thought about testing out and it involves asking the model to re-construct the problem using a logic language (like Prolog) and asking it to execute this type of program in order to come up with a solution rather than attempting simple chain-of-reason training / other methodologies of getting the model to 'reason' through some of these examples. People forget t…

I took one of the problems that gpt4o got wrong, and asked gpt4o what tools it could use. It suggested and wrote prolog for me that (with one pass back to get things defined in the right order) which correctly worked out the answer.

Re: Simple tasks showing reasoning breakdown in state-of-the-art LLMs

#277

Post by IanCal »

sollewitt wrote: post #109
photon_lines wrote: post #44

Earlier quoted context omitted.

There's actually a pretty simple solution to this that I thought about testing out and it involves asking the model to re-construct the problem using a logic language (like Prolog) and asking it to execute this type of program in order to come up with a solution rather than attempting simple chain-of-reason training / other methodologies of getting the model to 'reason' through some of these examples. People forget t…

Right, and do you verify the result? You have to know what the answer is supposed to be before you can write a test case.

I can have more confidence that my calculations are correct using a calculator compared to doing it by hand, even if I don't know the exact right answer beforehand.

Re: Simple tasks showing reasoning breakdown in state-of-the-art LLMs

#278

Post by IanCal »

asadotzler wrote: post #254
photon_lines wrote: post #44

Earlier quoted context omitted.

There's actually a pretty simple solution to this that I thought about testing out and it involves asking the model to re-construct the problem using a logic language (like Prolog) and asking it to execute this type of program in order to come up with a solution rather than attempting simple chain-of-reason training / other methodologies of getting the model to 'reason' through some of these examples. People forget t…

If that kind of thing worked, we'd have been doing it long before LLM chatbots.

Yet tools like GPT4o can do this.

It's not a trivial problem, taking a human written description and rewriting it as a prolog program.

Re: Simple tasks showing reasoning breakdown in state-of-the-art LLMs

#279

Post by rep_lodsb »

kalkin wrote: post #133

> Paper finds a failure case for current LLMs and draws broad conclusions about the fundamental limits of LLM technology > Evaluates the largest LLMs and finds evidence that actually scale overcomes the problem: "Notable exceptions are Claude 3 Opus and GPT-4 that occasionally manage to provide correct responses backed up with correct reasoning as evident in structured step by step explanations those models deliver t…

The key word is "occasionally". For anything even close to AGI, it would be reasonable to expect a correct answer to such a simple question 100% of the time. Or at least not confidently wrong answers.
Post reply on HN