Chain-of-thought can hurt performance on tasks where thinking makes humans worse

by benocodes • 2 years ago

371 250 2 years ago

Tasks were thinking makes human worse

Three such cases are implicit statistical learning, visual recognition, and classifying with patterns containing exceptions.

Fascinating that our lizard brains are better at implicit statistical reasoning

Dilettante_2• 2 years ago

Well, by definition, thinking is always explicit reasoning, no?

And I'd hazard a guess that a well-thought through Fermi Estimation beats lizard-brain eyeballing every time, it's just that in the inbetween space the two interfere unfavourably.

YetAnotherNick• 2 years ago

My guess would be no. I have terrible face recognition ability and I can look into face for hour and still other people could easily beat me in less than a second.(I am assuming "well-thought through Fermi Estimation" would be similar for me and others in this case).

Terr_3• 2 years ago

Well, by definition, thinking is always explicit reasoning, no?

That doesn't feel right to me. (Heh, accidentally appropriate word choice.) There are a lot of tasks we do that are arguably "thinking" yet don't involve an internal "Oh, hey, I'm gonna solve this problem, I'm thinking right now."

For example, imagine you're at a park, and someone is feeding the ducks. Another person walks up behind them and sucker-punches them into the pond.

It should be almost a reflex [0] that you'll conclude "the puncher is bad" and "the person in the water needs help" without explicitly reasoning out. I think that task qualifies as "thinking", especially since it involves some kind of theory-of-mind about those other humans.

[0] An exception might be someone with a sociopathic disability, who would have to think more-explicitly to realize what reaction is expected of them.

brewii• 2 years ago

Think about how fast you’re able to determine the exact trajectory of a ball and location to place your hand to catch a ball using your lizard brain.

asah• 2 years ago

you mean like pingpong?

https://arstechnica.com/information-technology/2024/08/man-v...

taeric3• 2 years ago

This isn't some innate ability that people have. As evidenced by how bad my kids are at catching things. :D

That said, I think this is a good example. We call it "muscle memory" in that you are good at what you have trained at. Change a parameter in it, though, and your execution will almost certainly suffer.

hangonhn• 2 years ago

You can do this while you're staring up the whole time. Your brain can predict where the ball will end up even though it's on a curved trajectory and place your hand in the right spot to catch it without guidance from your eyes in the final phase of travel. I have very little experience playing any kind of sport that involves a ball and can reliably do this.

newZWhoDis• 2 years ago

Which funny enough is why I hate rocket league.

All those years of baseball as a kid gave me a deep intuition for where the ball would go, and that game doesn’t use real gravity (the ball is too floaty).

melenaboija3• 2 years ago

Well, think how a bug and its shitty brain flies and avoids all type of obstacles amazingly fast.

This kind of things make me think LLMs are quite far from AGI.

GuB-42• 2 years ago

Or even more impressively, how you can pick up a random object and throw it with some accuracy.

Catching a ball is easy by comparison, also, my dog is better than I am at this game.

But throwing a random object not only requires an estimation of the trajectory, but also estimating the mass and aerodynamic properties in advance, to properly adjust the amount of force the throw will use as well as the release point with high accuracy. Doing it with baseballs is "easy", as the parameters are all well known and pitchers spend considerable time training. But picking an oddly shaped rock or stick you have never seen before and throw it not completely off target a second later, now we are talking.

jmd42• 2 years ago

I recall a study which suggested that we don't really calculate the trajectory as such, but use some kind of simple visual heuristic to continually align ourselves with where the ball is going to land.

They showed that people running to catch a ball would follow an inefficient curved path as a result of this, rather than actually calculating where the ball will land and moving there in a straight line to intercept it.

daft_pink• 2 years ago

this is exactly what I was looking for. tasks where I should not think and just trust my gut.

m3kw9• 2 years ago

would be slow to use COT on simple requests like 1+1

ryoshu• 2 years ago

95% * 95% = 90.25%

gpsx• 2 years ago

I saw an LLM having this kind of problem when I was doing some testing a ways back. I asked it to order three fruits from largest to smallest. I think it was orange, blueberry and grapefruit. It could do that easily with a simple prompt. When the prompting included something to the effect of “think step by step”, it would try to talk through the problem and it would usually get it wrong.

spockz• 2 years ago

How much does this align with how we learn math? We kind of instinctively learn the answers to simple math questions. We can even at some point develop an intuition for things like integrating and differentials. But the moment we are asked to explain why, or worse provide a proof, things become a lot harder. Even though the initial answer may be correct.

larodi2• 2 years ago

I definitely don’t learn math by means of gradient descents.

We can possibly say math is not learned, but a mental models of abstractions are developed. How? We dunno, but what we do know is we don’t learn by figuring the common features between all previously seen equations only to guess them later…

Mind operates on higher and higher levels of abstractions building on each other in a much fascinating way, very often not with words, but with structure and images.

Of course there are people with aphantasia, but i really fail to see how any reasoning happens in purely language level. Someone on this forum also noted - in order to reason one needs an ontology to facilitate the reasoning process. LLMs don’t do ontologies…

And finally, not least though, LLM and ML people in general seem to equate intuition to some sort biased.random(). Well intuition is not random, and is hard to describe in words. So are awe and inspiration. And these ARE part of (precondition to, fuel for) humanity’s thought process more that we like to admit.

mplewis3• 2 years ago

Humans learn skills like basic mathematics by reasoning about their environment and building internal models of problems they’re trying to solve. LLMs do not reason and they cannot model their environment.

dev_0• 2 years ago

From Claude:

I'll rank those three fruits from largest to smallest:

1. Grapefruit 2. Orange 3. Blueberry

The grapefruit is definitely the largest of these three fruits - they're typically around 4-6 inches in diameter. Oranges are usually 2-3 inches in diameter, and blueberries are the smallest at roughly 0.5 inches in diameter.

mromanuk• 2 years ago

chatGPT, from smaller to largest: Blueberry Orange Grapefruit

ajuc• 2 years ago

It's not thinking, it compressed the internet into a clever, lossy format with nice interface and it retrieves stuff from there.

Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

Eisenstein2• 2 years ago

These kind of reductive thought-terminating cliches are not helpful. You are using a tautology (it doesn't think because it is retrieving data and retrieving data is not thinking) without addressing the why (why does this preclude thinking) or the how (is it doing anything else to generate results).

easyThrowaway• 2 years ago

I have no idea how accurate it actually is, But I've had the process used by LLM described as the following: "Think of if like a form of UV Mapping, applied to language constructs rather than 3D points in space, and the limitations and approximations you experience are similar to those emerging when having to project a 2D image over a 3D surface."

bongodongobob• 2 years ago

Maybe if you bury your head in the sand AI will go away. Good luck!

Jerrrrrrry7• 2 years ago

  >It's not thinking



  >it compressed the internet into a clever, lossy format with nice interface and it retrieves stuff from there.

Humans do both, why can't LLM's?

  >Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

More like pulling out a deep-fried meme, looking for context, then searching google images until you find the most "original" JPG representation with the least amount of artifacts.

There is more data to add confidently, it just has to re-think about it with a renewed perspective, and an abstracted-away higher-level context/attention mechanism.

danenania2• 2 years ago

Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

Empirically speaking, I have a set of evals with an objective pass/fail result and a prompt. I'm doing codegen, so I'm using syntax linting, tests passing, etc. to determine success. With chain-of-thought included in the prompting, the evals pass at a significantly higher rate. A lot of research has been done demonstrating the same in various domains.

If chain-of-thought can't improve quality, how do you explain the empirical results which appear to contradict you?

not_a_bot_4sho• 2 years ago

I always found it interesting how sorting problems can get different results when you add additional qualifiers like colors or smells or locations, etc.

Natively, I understand these to influence the probability space enough to weaken the emergence patterns we frequently overestimate.

Jerrrrrrry7• 2 years ago

The model is likely to had already seen the exact phrase from its last iteration. Adding variation generalizes the inference away from over-trained quotes.

Every model has the model before it, and it's academic papers, in it's training data.

Changing the qualifiers pulls the inference far away from quoting over-trained data, and back to generalization.

I am sure it has picked up on this mesa-optimization along the way, especially if I can summarize it.

Wonder why it hasn't been more generally intelligent, yet.

ianbicking• 2 years ago

It would be interesting to think about how it got it wrong. My hunch is that in the "think step by step" section it made an early and incorrect conclusion (maybe even a subtly inferred conclusion) and LLMs are terrible at walking back mistakes so it made an internally consistent conclusion that was incorrect.

A lot of CoT to me is just slowing the LLM down and keeping it from making that premature conclusion... but it can backfire when it then accidentally makes a conclusion early on, often in a worse context than it would use without the CoT.

fxnn• 2 years ago

Maybe it needs even smaller steps, and a programmatic (i.e. multi prompt) habit to always double-check / validate the assumptions and outcomes.

npunt2• 2 years ago

"Don't overthink it" is sometimes good advice!

marviel2• 2 years ago

I love backpropagating ideas from ML back into psychology :)

I think it shows great promise as a way to sidestep the ethical concerns (and the reproducibility issues) associated with traditional psychology research.

One idea in this space I think a lot about is from the Google paper on curiosity and procrastination in reinforcement learning: https://research.google/blog/curiosity-and-procrastination-i...

Basically the idea is that you can model curiosity as a reward signal proportional to your prediction error. They do an experiment where they train an ML system to explore a maze using curiosity, and it performs the task more efficiently -- UNTIL they add a "screen" in the maze that shows random images. In this case, the agent maximizes the curiosity reward by just staring at the screen.

Feels a little too relatable sometimes, as a highly curious person with procrastination issues :)

npunt2• 2 years ago

"...in AI" will be the psychology equivalent of biology's "...in Mice"

jeezfrk• 2 years ago

"Nerd sniping"

veryfancy• 2 years ago

So like dating?

Terr_3• 2 years ago

Alternate framing: A powerful autocomplete algorithm is being used to iteratively extend an existing document based on its training set. Sometimes you get a less-desirable end-result when you intervene to change the style of the document away from question-and-answer to something less common.

youoy• 2 years ago

That's what one half of HN think. The other half:

Artificial brains in the verge of singularity show another sign of approaching consciousness. The chain of thought of process performance is exactly human, showing yet another proof of the arrival of AGI before 2030.

lazide• 2 years ago

Pfft, 2030?!? It’s already in the middle of manipulating the election! (/s, kinda)

fiso64• 2 years ago

A framing that is longer, far harder to parse, and carries less information.

grain-o-salt• 2 years ago

Let me give it a try...um...what about 'Star Trek' vs.: A delivering-service called Galaxyray?galaxyray brings wares and hot tasty meals galaxywide to recipients, even while they are 'traveling' with more-than-lightspeed in hyperspace?

..ordered by Imperium just to troll the retros!?

Sounds "less comon"...hu...?! P-:

Ok! Ok! let me try to explain it a bit more, the whole Universe projected as a beam, say... scalable, 100m, placed in a storage depot, a 'parralaxy' ...So delivery agents are grabbing the ordered stuff and...no? Not?

As reasonable like your answer is, do that sound very 'uncommon' while 'phrasing that with many questionmarks'?

Enjoying my day off... (-: regards,

Y_Y2• 2 years ago

Reminds me of a mantra from chess class:

   long think = wrong think

TZubiri7• 2 years ago

Was that perhaps a speed chess class?

hackable_sand• 2 years ago

I prefer to call it Kung fu

Because you feel like a martial artist.

Y_Y2• 2 years ago

Nope, just vanilla otb slow chess

spongebobism• 2 years ago

The original by Bent Larsen is "Long variation, wrong variation"

meowster• 2 years ago

Think long; think wrong

( Flows off the tongue better ¯\_(ツ)_/¯ )

TZubiri7• 2 years ago

So, LLMs face a regression on their latest proposed improvement. It's not surprising considering their functional requirements are:

1) Everything

For the purpose of AGI, LLM are starting to look like a local maximum.

rjbwork• 2 years ago

For the purpose of AGI, LLM are starting to look like a local maximum.

I've been saying it since they started popping off last year and everyone was getting euphoric about them. I'm basically a layman - a pretty good programmer and software engineer, and took a statistics and AI class 13 years ago in university. That said, it just seems so extremely obvious to me that these things are likely not the way to AGI. They're not reasoning systems. They don't work with axioms. They don't model reality. They don't really do anything. They just generate stochastic output from the probabilities of symbols appearing in a particular order in a given corpus.

It continues to astound me how much money is being dumped into these things.

ChadNauseam2• 2 years ago

How do you know that they don’t do these things? Seems hard to say for sure since it’s hard to explain in human terms what a neural network is doing.

alexwebb25• 2 years ago

If you expect "the right way" to be something _other_ than a system which can generate a reasonable "state + 1" from a "state" - then what exactly do you imagine that entails?

That's how we think. We think sequentially. As I'm writing this, I'm deciding the next few words to type based on my last few.

Blows my mind that people don't see the parallels to human thought. Our thoughts don't arrive fully formed as a god-given answer. We're constantly deciding the next thing to think, the next word to say, the next thing to focus on. Yes, it's statistical. Yes, it's based on our existing neural weights. Why are you so much more dismissive of that when it's in silicon?

csomar• 2 years ago

I am pretty sure Claude 3.5 Sonnet can reason or did reason with a particular snippet of code I was working on. I am not an expert in this area but my guessing is that these neural nets (made for language prediction) are being used for reasoning. But that’s not their optimal behavior (since they are token predictor). A big jump in reasoning will happen when reasoning is off loaded to an LRM.

Human brains are sure big but they are inefficient because a big portion of the brain is going to non-intelligence stuff like running the body internal organs, eye vision, etc…

I do agree that the money is not well spent. They should haver recognized that we are hitting s local maximum with the current model and funding should be going to academic/theoretical instead of dump brute force.

wyldfire• 2 years ago

It continues to astound me how much money is being dumped into these things.

Maybe in our society there's a surprising amount of value of a "word stirrer" intelligence. Sure, if it was confident when it was right and hesitant when it was wrong it'd be much better. Maybe humans are confidently wrong often enough that an artificial version that's compendious experience to draw on is groundbreaking.

chamomeal• 2 years ago

I totally agree that they’re a local maximum and they don’t seem like a path to AGI. But they’re definitely kinda reasoning systems, in the sense that they can somewhat reason about things. The whacky process they use to get there doesn’t take away from that IMO

kibwen• 2 years ago

I've been saying it since they started popping off last year and everyone was getting euphoric about them.

Remember the resounding euphoria at the LK-99 paper last year, and how everyone suddenly became an expert on superconductors? It's clear that we've collectively learned nothing from that fiasco.

The idea of progress itself has turned into a religious cult, and what's worse, "progress" here is defined to mean "whatever we read about in 1950s science fiction".

jsheard• 2 years ago

So, LLMs face a regression on their latest proposed improvement.

Arguably a second regression, the first being cost, because COT improves performance by scaling up the amount of compute used at inference time instead of training time. The promise of LLMs was that you do expensive training once and then run the model cheaply forever, but now we're talking about expensive training followed by expensive inference every time you run the model.

TZubiri7• 2 years ago

To be fair they also advanced in the cost aspect with other models

gpt4o and 4o mini have a tenth and a hundredth of inference cost of gpt4 respectively

idiotsecant3• 2 years ago

LLMs are a local maximum in the same way that ball bearings can't fly. LLM-like engines will almost certainly be components of an eventual agi-level machine.

TZubiri7• 2 years ago

I agree, my bet is that they will be used for NLP, and ML debugging/analysis.

FuckButtons2• 2 years ago

I don’t think that’s necessarily true, that presumes that the cobbled together assortment of machine learning algorithms we have now will somehow get agi, if we need a fundamentally different way of doing things there’s no reason to assume it will use a language model at all.

lucianbr4• 2 years ago

What is your "almost certainty" based on? What does it even mean? Every thread on LLMs is full of people insisting their beliefs are certainties.

What I'm certain is we should not praise the inventor of ball bearings for inventing flight, nor once ball bearings were invented flight became unavoidable and only a matter of time.

pessimizer2• 2 years ago

So, LLMs face a regression on their latest proposed improvement.

A regression that humans also face, and we don't say therefore that it is impossible to improve human performance by having them think longer or work together in groups, we say that there are pitfalls. This is a paper saying that LLMs don't exhibit superhuman performance.

mitko2• 2 years ago

This is so uncannily close to the problems we're encountering at Pioneer, trying to make human+LLM workflows in high stakes / high complexity situations.

Humans are so smart and do so many decisions and calculations on the subconscious/implicit level and take a lot of mental shortcuts, so that as we try to automate this by following exactly what the process is, we bring a lot of the implicit thinking out on the surface, and that slows everything down. So we've had to be creative about how we build LLM workflows.

lolinder• 2 years ago

This is a regression in the model's accuracy at certain tasks when using COT, not its speed:

In extensive experiments across all three settings, we find that a diverse collection of state-of-the-art models exhibit significant drop-offs in performance (e.g., up to 36.3% absolute accuracy for OpenAI o1-preview compared to GPT-4o) when using inference-time reasoning compared to zero-shot counterparts.

In other words, the issue they're identifying is that COT is an less effective model for some tasks compared to unmodified chat completion, not just that it slows everything down.

mitko2• 2 years ago

Yeah! That's the danger with any kind of "model" whether it is CoT, CrewAI, or other ways to outsmart it. It is betting that a programmer/operator can break a large tasks up in a better way than an LLM can keep attention (assuming it can fit the info in the context window).

ChatGPT's o1 model could make a lot of those programming techniques less effective, but they may still be around as they are more manageable, and constrained.

haccount2• 2 years ago

Language seems to be confused with logic or common sense.

We've observed it previously in psychiatry(and modern journalism, but here I digress) but LLMs have made it obvious that grammatically correct, naturally flowing language requires a "world" model of the language and close to nothing of reality, spatial understanding? social clues? common sense logic? or mathematical logic? All optional.

I'd suggest we call the LLM language fundament a "Word Model"(not a typo).

Trying to distil a world model out of the word model. A suitable starting point for a modern remake of Plato's cave.

PedroBatista• 2 years ago

It’s in the name: Language Model, nothing else.

repeekad3• 2 years ago

Hi I’m just a random internet stranger passing by and was intrigued by Plato’s Cave as I’m not a fancy person who reads books. GPT-4o expanded for you quite well, but I’m not sure how I feel about it…

Using AI how I just did feels like cheating on an English class essay by using spark notes, getting a B+, and moving right on to the next homework assignment.

On one hand, I didn’t actually read Plato to learn and understand this connection, nor do I have a good authority to verify if this output is a good representation of his work in the context of your comment.

And yet, while I’m sure students could always buy or loan out reference books to common student texts in school, AI now makes this “spark notes” process effectively a commodity for almost any topic, like having a cross-domain low-cost tutor instantly available at all time.

I like the metaphor that calculators did to math what LLMs will do for language, but I don’t really know what that means yet

GPT output:

“““ The reference to Plato’s Cave here suggests that language models, like the shadows on the wall in Plato’s allegory, provide an imperfect and limited representation of reality. In Plato’s Cave, prisoners are chained in a way that they can only see shadows projected on the wall by objects behind them, mistaking these shadows for the whole of reality. The allegory highlights the difference between the superficial appearances (shadows) and the deeper truth (the actual objects casting the shadows).

In this analogy, large language models (LLMs) produce fluent and grammatically correct language—similar to shadows on the wall—but they do so without direct access to the true “world” beyond language. Their understanding is derived from patterns in language data (“Word Model”) rather than from real-world experiences or sensory information. As a result, the “reality” of the LLMs is limited to linguistic constructs, without spatial awareness, social context, or logic grounded in physical or mathematical truths.

The suggestion to call the LLM framework a “Word Model” underscores that LLMs are fundamentally limited to understanding language itself rather than the world the language describes. Reconstructing a true “world model” from this “word model” is as challenging as Plato’s prisoners trying to understand the real world from the shadows. This evokes the philosophical task of discerning reality from representation, making a case for a “modern remake of Plato’s Cave” where language, not shadows, limits our understanding of reality. ”””

elif5• 2 years ago

Language is the tool we use to codify a heuristic understanding of reality. The world we interact with daily is not the physical one, but an ideological one constructed out of human ideas from human minds. This is the world we live in and the air we breath is made of our ideas about oxygenation and partly of our concept of being alive.

It's not that these "human tools" for understanding "reality" are superfluous, it's just that they ar second-order concepts. Spatial understandings, social cues, math, etc. Those are all constructs built WITHIN our primary linguistic ideological framing of reality.

beardedwizard2• 2 years ago

I am baffled that people have to continue making this argument over and over and over. Your rationale makes total sense to me, but the debate rages on whether or not LLMs are more than just words.

Articles like this only seem to confirm that any reasoning is an illusion based on probabilistic text generation. Humans are not carefully writing out all the words of this implicit reasoning, so the machine cant appear to mimic them.

What am I missing that makes this debatable at all?

kbrisso• 2 years ago

Bingo, great reply! This is what I've been trying to explain to my wife. LLM's use fancy math and our language examples to reproduce our language but have no thoughts are feelings.

jumping_frog2• 2 years ago

There is a four part documentary by Stephen Fry called "Planet Word". Worth watching.

TylerE• 2 years ago

I sometimes wonder how they’d do if trained on relatively rigid, language like Japanese that has far fewer ambiguities than English.

1317• 2 years ago

why are Pioneer doing anything with LLMs? you make AV equipment

coding123• 2 years ago

pioneerclimate.com

nisten• 2 years ago

This sounds about right from my experience getting nerdsniped by new samplers along with trying to reproduce the API middleware for the whole reflection thing, and using 4400 questions for a new benchmark is not bad given that even the well-regarded gpqa benchmark is only 3000-something questions.

What's ... mildly infuriating here is the lack of any kind of data, code, 0 mention of github in the paper, and nothing for anyone to reproduce or find any reason in my opinion to even recommend anyone to read this thing at all. If you think that whatever you're doing in the field of LLMs won't be obsolete in 6 months you're being delusional.

Anyway, back to the paper, it says all questions culminated to a yes or no answer... meaning theres a 50/50 chance of getting right, so does that mean the 8% drop in performance you got from testing llama 3 8b this way is more like 4% which would make it statistically insignificant? And given that the only other scientifically usueful & reproducible (non-api walled models which no one knows on how many actual llms and retrieval systems are composing that solution you're testing)models were less than that leads me to the opinion that this whole thing was just useless slop.

So please, if you're writing a paper in LLMs, and want to seem credible, either have some type of demo thing or show the actual god damn trash code and top secret garbage data you wrote for it so people can make some kind of use of it before it goes obsolete otherwise you're just wasting everyones time.

TL:DR. It's trash.

alexchantavy• 2 years ago

This seems to support how thinking out loud during a coding test might make you do worse.

why-el• 2 years ago

I like this analogy a lot. It's possible that forced externalization of thoughts accidentally causes the omission of crucial data. That is, much more goes on in your head, you probably laid out the whole algorithm, but being asked to state it on the spot and in clear, serial words is causing you to bork it by taking shortcuts.

jwpapi• 2 years ago

This is so interesting. What are even the tasks where thinking makes humans worse?

sigmoid10• 2 years ago

The answer is in the article. One example they give is grammar. Lots of people apparently do worse once they try to verbalize it.

XCSme3• 2 years ago

What are even the tasks where thinking makes humans worse?

Not really related, but athletes perform A LOT worse when they are thinking about their movements/strategies/tactics. A top performing athlete does best when they are in a flow state, where they don't think about anything and just let their body/muscle memory do the work.

Once you start thinking about micro-adjustments (e.g. I should lift my elbow higher), you start controlling your body in a conscious way, which is a magnitude slower and less coordinated than the automatic/subconscious way.

Also, same happens for creativity/new ideas. If you intentionally think about something, step by step, you won't likely find new, innovative solutions. There is a reason why the "a-ha!" moments come in the shower, your subconscious mind is thinking about the problem instead of trying to force your thinking on a specific path.

I would guess this happens in many other areas, where channelling the thought process through a specific template hinders the ability to use all the available resources/brain power.

naasking7• 2 years ago

What are even the tasks where thinking makes humans worse?

Talking about religion and politics.

sowbug• 2 years ago

I can think myself into forgetting strong passwords if I try to spell each character out in my head. But then I sit at a keyboard, relax, and automatically type it perfectly.

lucianbr4• 2 years ago

Muscle memory or something like it hardly seems a step towards AGI. Or towards solving any difficult problems.

wg02• 2 years ago

Not to mention that chain of thought is computationally very expensive. Prohibitively expensive for sure to be served free like previous generation of Web 2.0 products.

Seems like repeated promoting can't juice AGI out of token probabilities.

Retrospectively, if you can pin point one paper that led to the bust and pop of the AI bubble, this would be it.

dev1ycan• 2 years ago

Stop dumping billions of your own money (if you are a VC) in LLMs, you are going to regret it in the long run. You are funding con-artist's salaries...

cainxinth• 2 years ago

This says something fascinating about information processing in both biological and AI systems. Both systems compress information: the brain creates efficient neural patterns through experience and AI develops internal representations through training. Forcing verbalization "decompresses" this efficient encoding, potentially losing subtle patterns. Hence, for a task like visual recognition, which is optimized to occur almost instantly in a parallel process, you will only degrade performance by running it in a serial chain of thought sequence.

Chain-of-thought can hurt performance on tasks where thinking makes humans worse

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