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Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

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Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#11

Post by arghnoname »

pottspotts wrote: post #7
RayVR wrote: post #2

This aligns well with my personal experience using gpt-4. The model provides surprisingly good responses on topics which I know are readily available online while being potentially troublesome to find the exact information I want. I have even found it useful when I know there is a tool for what I want but can’t recall the jargon used to find it via Google. Simply describing the rough idea is enough to get the model t…

For a significant number of software developers, GPT and Github's Copilot have replaced StackOverflow, and even Googling more generally. It is more than an autocomplete, it is the best resource for software development by far, IMO. It's a tutor that's an expert in virtually every topic.

When people just ask chatGPT for solutions and there's no community, a la stack overflow, where will it get the answers to future problems?

If chatGPT is too successful and people stop producing content because chatGPT is too successful, it might end up in a local optima that isn't so optimal.

Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#12

Post by jltsiren »

TOMDM wrote: post #6
Closi wrote: post #3

I think one main failure in the framing of these papers (and discussion of LLMs more broadly) is that the abstract says that GPT4 ‘struggles’ with logical reasoning: > ChatGPT and GPT-4 do relatively well on well-known datasets […] however, the performance drops significantly when handling newly released and out-of-distribution [where] Logical reasoning remains challenging for ChatGPT and GPT-4 But reading the paper…

I think a lot of these LLM benchmarks should include a human avg, otherwise I don't really have a frame of reference other than personal experience with the models.

Human average can be a misleading statistic, because the average human is useless for almost everything. In almost every job, the average person doing the job is well above the average (in the general population) for that particular job.

Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#13

Post by svachalek »

arghnoname wrote: post #11
pottspotts wrote: post #7

Earlier quoted context omitted.

For a significant number of software developers, GPT and Github's Copilot have replaced StackOverflow, and even Googling more generally. It is more than an autocomplete, it is the best resource for software development by far, IMO. It's a tutor that's an expert in virtually every topic.

When people just ask chatGPT for solutions and there's no community, a la stack overflow, where will it get the answers to future problems? If chatGPT is too successful and people stop producing content because chatGPT is too successful, it might end up in a local optima that isn't so optimal.

Well, the difference is coming from both directions. ChatGPT is pretty amazing, but Stack Overflow has been self destructing for many years ahead of this.

Likely the future of training these systems will come from interacting with their users and perhaps directly with the tools and compilers too. They can learn from that without needing a new corpus of human-human interactions.

Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#14

Post by outofpaper »

raydiatian wrote: post #8
RayVR wrote: post #2

This aligns well with my personal experience using gpt-4. The model provides surprisingly good responses on topics which I know are readily available online while being potentially troublesome to find the exact information I want. I have even found it useful when I know there is a tool for what I want but can’t recall the jargon used to find it via Google. Simply describing the rough idea is enough to get the model t…

I have been thinking for a few weeks now that we need another term for large language models trained on colossal datasets: AGK, artificially generally/globally knowledgeable. It can mimic a likeness of problem solving because the corpus it was trained on is full of problem/solution pairs in the abstract. But task it with any novel problem solving challenge outside of its training that is of sufficient complexity and…

Often it can actually solve more complex problems but needs to have its "hand held". Essentially the model needs to be guided to/through problem solving techniques. We have to remember that LLM are literally inference engines. They default to providing us with probable results, probable responses. They can pe pulled away from these "knee jerk" responses.

Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#15

Post by skybrian »

Closi wrote: post #3

I think one main failure in the framing of these papers (and discussion of LLMs more broadly) is that the abstract says that GPT4 ‘struggles’ with logical reasoning: > ChatGPT and GPT-4 do relatively well on well-known datasets […] however, the performance drops significantly when handling newly released and out-of-distribution [where] Logical reasoning remains challenging for ChatGPT and GPT-4 But reading the paper…

You're framing this as if there were a single yes-or-no question that we should all agree on. (Are the LLM's "any good?")

But in real-world contexts, there are some tasks that just about anyone could do, others where "average" human performance isn't good enough and you need to hire an expert, and also some jobs that can only be done by machine.

So it seems like the bar should be set based on what you think is necessary for whatever practical application you have in mind?

If it's just a game, beating an average chess player, someone who is really good, or the best in the world are different milestones. And for chess there is an ELO ranking system that lets you answer this more precisely, too.

A paper about how well chatbots do on some reasoning tests can't answer this for you.

Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#16

Post by famouswaffles »

raydiatian wrote: post #8
RayVR wrote: post #2

This aligns well with my personal experience using gpt-4. The model provides surprisingly good responses on topics which I know are readily available online while being potentially troublesome to find the exact information I want. I have even found it useful when I know there is a tool for what I want but can’t recall the jargon used to find it via Google. Simply describing the rough idea is enough to get the model t…

I have been thinking for a few weeks now that we need another term for large language models trained on colossal datasets: AGK, artificially generally/globally knowledgeable. It can mimic a likeness of problem solving because the corpus it was trained on is full of problem/solution pairs in the abstract. But task it with any novel problem solving challenge outside of its training that is of sufficient complexity and…

Reading the paper it seems they are problems a lot of people would fail at it too, at least some of the time. LLMs are not superhuman in logical reasoning seems to be the conclusion more than anything.

Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#17

Post by mftb »

pottspotts wrote: post #7
RayVR wrote: post #2

This aligns well with my personal experience using gpt-4. The model provides surprisingly good responses on topics which I know are readily available online while being potentially troublesome to find the exact information I want. I have even found it useful when I know there is a tool for what I want but can’t recall the jargon used to find it via Google. Simply describing the rough idea is enough to get the model t…

For a significant number of software developers, GPT and Github's Copilot have replaced StackOverflow, and even Googling more generally. It is more than an autocomplete, it is the best resource for software development by far, IMO. It's a tutor that's an expert in virtually every topic.

Yea it's not. Sorry to contradict, but it's not like that. In any kind of tutoring arrangement you're time with them is limited, and if they're any good, they don't just regurgitate limitless example code. Two of the most important decisions that an instructor has to make are, how much access to give you, and how much example material to give you, because the actual learning begins when you have to think for yourself, and you are forced to confront a black screen with a flashing cursor, and fill it with your own ideas. So interacting with ChatGPT may be a great experience, but it's not that. Maybe someday it will be.

Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#18

Post by mrshadowgoose »

pottspotts wrote: post #5
Closi wrote: post #3

I think one main failure in the framing of these papers (and discussion of LLMs more broadly) is that the abstract says that GPT4 ‘struggles’ with logical reasoning: > ChatGPT and GPT-4 do relatively well on well-known datasets […] however, the performance drops significantly when handling newly released and out-of-distribution [where] Logical reasoning remains challenging for ChatGPT and GPT-4 But reading the paper…

The goal posts for AI are moving quickly, and in my mind, a lot of the criticism os too shallow. People want it to perform better than any expert human at any possible subject before it's considered "real AI". It isn't enough for critics for it to be better than the average person at virtually everything its put to the test on. It seems like there is some resentment and almost anger at this technology, particularly w…

> It seems like there is some resentment and almost anger at this technology, particularly with the artistic AIs like Midjourney. I can understand that more readily, but what's the real beef with ChatGPT?

People seem to have a real tough time accepting that human brains might not be that special. They see things like GPT-4, and tend to fall into soothing mental traps to rationalize that innate but baseless rejection. I actually view all the sustained anger and resentment as a signal that we are making meaningful inroads into AGI, as it means that people are actually being impacted.

One of the most common mental traps is "It's just fancy autocomplete." People tend to stop there and don't proceed to consider that the veracity of that claim is irrelevant. Autocomplete or not, GPT-4 seems to be able to provide meaningful assistance to certain workflows that were previously only within the bounds of human cognition.

> People want it to perform better than any expert human at any possible subject before it's considered "real AI". It isn't enough for critics for it to be better than the average person at virtually everything its put to the test on.

It's quite amusing that some people have moved their goalposts to "well it's not a superintelligence, therefore it's worthless". Simultaneously, it's highly depressing, because it means various actors will likely achieve AGI while the rest of us are still bickering about autocomplete and Chinese rooms.

Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#19

Post by svachalek »

Closi wrote: post #3

I think one main failure in the framing of these papers (and discussion of LLMs more broadly) is that the abstract says that GPT4 ‘struggles’ with logical reasoning: > ChatGPT and GPT-4 do relatively well on well-known datasets […] however, the performance drops significantly when handling newly released and out-of-distribution [where] Logical reasoning remains challenging for ChatGPT and GPT-4 But reading the paper…

If you really force it to reason, rather than regurgitate arguments from its training set, you will find it is nowhere near the genius line. Make up some rules and have it try to answer questions according to the rules. In my experiments I feel it's something like a 4 or 5 year old child both in its logical limitations and penchant for distraction.

However it's important to note one VERY important thing -- this is not a system that is designed to reason! At all, as far as I know. That just fell out of its ability for language somehow. So to just accidentally be able to reason like a 4 year old human (which are vastly clever compared to the adult of any other animal species I'm aware of) is incredibly impressive and I think the next obvious step is to couple this tech together with some classic computing, which has far exceeded human capabilities for logic and reason for decades already. If ChatGPT has some secondary system for reasoning and just uses the LLM for setting up problems and reading results, I think it could reach superhuman levels of reasoning quite easily.

Re: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

#20

Post by causality0 »

They're good in "memory" reasoning but terrible in deductive reasoning. Like if you say there's a sign in front of a door saying "push" it will tell you you need to push the door, but if you say there was a powerful wind and you see a sign saying "pull" laying on the ground on the other side of a glass door it has no idea if you should push or pull.
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