When LLMs Misinterpret Extraneous Details
Key Points
- Jeff presents a simple kiwi‑counting problem with an unnecessary detail (“five of them are smaller”) and the AI incorrectly subtracts five, illustrating how LLMs can be tripped up by extraneous information.
- The mistake stems from probabilistic pattern matching: the model recalls training examples where similar caveats always altered the answer, so it automatically applies the pattern instead of evaluating the math.
- This example shows that LLMs often produce correct‑looking results without genuine understanding, relying on statistical token prediction rather than true logical reasoning.
- The conversation expands to the philosophical question of whether AI (or we ourselves) are merely simulating thought, hinting at broader concerns about hallucination and “simulation” of reality.
- A related technical issue is token bias—small changes in input tokens can dramatically shift the model’s reasoning, highlighting the fragility of current LLM reasoning capabilities.
Sections
- LLM Misinterpretation of Extraneous Details - A conversation demonstrates how a language model incorrectly adjusts a simple math sum due to an irrelevant qualifier, exposing its reliance on probabilistic pattern matching from training data.
- LLMs: Sophisticated Autocomplete Mechanism - The speaker explains that large language models predict the next token, so tiny prompt tweaks can dramatically alter output, making their seeming reasoning akin to advanced autocomplete that can produce hallucinations.
- Inference-Time Reasoning in LLMs - The speakers explain how modern language models use additional compute at inference to “think” through chain‑of‑thought processes, enabling reasoning improvements without re‑training and sparking debate over genuine thought versus algorithmic simulation.
Full Transcript
# When LLMs Misinterpret Extraneous Details **Source:** [https://www.youtube.com/watch?v=CB7NNsI27ks](https://www.youtube.com/watch?v=CB7NNsI27ks) **Duration:** 00:08:48 ## Summary - Jeff presents a simple kiwi‑counting problem with an unnecessary detail (“five of them are smaller”) and the AI incorrectly subtracts five, illustrating how LLMs can be tripped up by extraneous information. - The mistake stems from probabilistic pattern matching: the model recalls training examples where similar caveats always altered the answer, so it automatically applies the pattern instead of evaluating the math. - This example shows that LLMs often produce correct‑looking results without genuine understanding, relying on statistical token prediction rather than true logical reasoning. - The conversation expands to the philosophical question of whether AI (or we ourselves) are merely simulating thought, hinting at broader concerns about hallucination and “simulation” of reality. - A related technical issue is token bias—small changes in input tokens can dramatically shift the model’s reasoning, highlighting the fragility of current LLM reasoning capabilities. ## Sections - [00:00:00](https://www.youtube.com/watch?v=CB7NNsI27ks&t=0s) **LLM Misinterpretation of Extraneous Details** - A conversation demonstrates how a language model incorrectly adjusts a simple math sum due to an irrelevant qualifier, exposing its reliance on probabilistic pattern matching from training data. - [00:03:02](https://www.youtube.com/watch?v=CB7NNsI27ks&t=182s) **LLMs: Sophisticated Autocomplete Mechanism** - The speaker explains that large language models predict the next token, so tiny prompt tweaks can dramatically alter output, making their seeming reasoning akin to advanced autocomplete that can produce hallucinations. - [00:06:08](https://www.youtube.com/watch?v=CB7NNsI27ks&t=368s) **Inference-Time Reasoning in LLMs** - The speakers explain how modern language models use additional compute at inference to “think” through chain‑of‑thought processes, enabling reasoning improvements without re‑training and sparking debate over genuine thought versus algorithmic simulation. ## Full Transcript
Jeff, I have a question for you, and that is, can I really think?
Well, Martin, let me answer your question with a math problem.
Exactly the response I would expect from an IBM Distinguished Engineer.
Go on.
I wouldn't want to disappoint.
So here's your math problem Martin: Oliver picks 44 kiwis on Friday,
Take notes.
Okay, 44.
Then he picks 58 kiwis on Saturday.
All right, 58.
On Sunday, he picks double the number that he picked on Friday,
But five of those are smaller.
Doesn't matter, 88.
So what's the total?
Well, that adds up to 190.
Well, Martin, according to my AI chat bot, you forgot to subtract
the fact that I told you five of them were smaller, so it should have been 185.
Smaller or not, they still count.
I think you need a new chat bot.
Okay, I agree.
You know, the fact that five of these were smaller doesn't really change the total.
But a research paper recently turned up that some LLMs got tripped up by these kinds of extraneous details.
How could an AI that seems so smart make such an obvious mistake?
Yeah, I've seen that paper,
and it all comes down to training data.
The paper proposes the LLMs perform something called probabilistic pattern matching.
I'll take notes on this.
Thank you.
And that means they search to find the closest data in the training dataset that matches the data.
They're looking for similar examples of, well, in this case, math problems.
And most of the time, when little details like five were smaller than average,
when that appears in a math problem, there's almost always a reason for that, right?
Almost every time a caveat like that was added in those math problems, in the training data,
the answer required taking that caveat into consideration.
Hence the LLM incorrectly electing to subtract five from the total
because that was the probabilistic pattern seen in most of the relevant training examples.
So, Martin, That brings us back to the broader question of Can I Really Think.
That is a good question.
Or is it really just simulating or imitating thought and reasoning
or, in fact, the broader question of are we all living in a simulation?
Is everything imitation?
Is anything actually real?
A slow down there, Jeff.
I think you're beginning to hallucinate like a chatbot,
but yes, this does imply that LLM pattern matching is coming at the expense of actual reasoning.
LLMs often come to the right answer without really having a proper understanding of the underlying concepts
which can lead to all sorts of issues like these.
So we saw how the extraneous details in the math problem I gave you were able to throw off in LLM,
but what else could cause models to struggle with reasoning like this?
Yeah LLMs struggle with logical reasoning because there's something called token bias.
I'll take a note on that.
Thanks.
Now, remember, these systems are effectively predicting the next word, or more accurately, the next token in a sequence,
and the reasoning output of the model changes when a single token of input changes,
which means that tiny little tweaks in how you prompt an LLM with a question
can have an outsized effect on the reasoning presented in the output.
So I suppose this is a bit like autocomplete on steroids.
If I say Mary had a little lamb, its fleece was white, as.
I'm going to say the autocomplete on that is snow, Jeff.
Well, no.
According to the autocomplete on my phone, it's Mary had a little lamb,
Its fleece was white as a little lamb, Yeah.
Kind of unimpressive.
Well, what would cause such a thing?
Well, the autocomplete uses a prediction scheme based on probabilities as to what the next word would be,
and LLMs do something similar as well, albeit with some additional smarts like attention.
And most of the time it's right,
but when it isn't, we get hallucinations and then, yeah
get weird extraneous details and stuff like this that you and I, we would quickly filter it out.
A chatbot that appears to be reasoning may actually just be doing a super sophisticated autocomplete where it's
guessing not only the next word, but also the next sentence, the next paragraph or even the entire document.
What a buzzkill, Martin.
I mean, we you just ruin the magic for all of us.
Thanks for that.
It's sort of like if I tell you when you see a magician saw a lady in a box in half and then I tell you, in fact,
there are two ladies, and you're just seeing the arms of one and the legs of the other.
Poof.
No more magic.
As with all things AI reasoning is evolving.
Look, we can smugly proclaim that I just doesn't understand concepts the way we superior humans do,
but some recent advancements are seeing big improvements in reasoning.
Most of Pre-training models today rely on something called training time compute.
Here, I'll take some notes.
No, thank you very much.
Now the models learned to reason,
or as we've seen, actually, they learned to perform probabilistic pattern matching during model training.
Then the model is released and now it's a fixed entity.
So the underlying model here, it doesn't change.
Now, remember, we talked about token bias, how small changes
in the input tokens, meaning your prompt can affect the reasoning in the output.
Well, that can actually be a good thing as we improve LLM reasoning through some prompt engineering techniques.
For example, a number of papers have shown significant LLM reasoning
improvements through something called chain of thought prompting.
Right. I've heard about that.
That's where you append things like things step by step to the prompt,
and that encourages the all of them to include reasoning steps before coming up with an answer.
Exactly right,
but the emphasis is on the person writing the prompts to use the right magic words,
the right incantations to get the LLM to adopt a chain of thought process.
What new models are doing is inference time.
Compute. It effectively tells the model to spend some time thinking before giving you an answer.
The amount of time it spends thinking is variable based on how much reasoning it needs to do.
A simple request might take a second or two.
Something longer might take several minutes only when it's completed its chain of thought
thinking period does it then start outputting an answer.
Basically, think before you speak.
Indeed,
and what makes inference time compute models interesting
is the inference reasoning is something that can be tuned
and improved without having to train and tweak the underlying model.
So there are now two places in the development of an LLM where reason can be improved,
at training time with better quality training data and that inference time with better chain of thought training.
Researchers at some of the AI labs are confident we'll see big
improvements in the reasoning of future LLM models because of this.
So, Martin, maybe we can finally get an AI that can actually count kiwis.
And that would be a glorious day,
but will it actually be thinking or just simulating thought a bunch of algorithms all running together?
I mean, after all, it's just a bunch of electrical circuits
and impulses running through those circuits at the end of the day, right?
Well, that's true.
But then so your thoughts are just a bunch of neurons firing electrical impulses in your brain,
and because we don't fully understand it, it seems almost magical.
Well, until I tell you how the magic trick works, which ruins the magic.
Sort of the way we think about AI as just tending to think and simulating thought
through using a bunch of algorithms that that's how the trick works.
But once you know how the trick works, then we have the question, is it really thought after all.
Is it really thought after all?
Jeff, you are asking a question for a philosopher, which I am not.
So I ask the next best thing, a popular chatbot, and we actually really like the response it came back with.
So I asked it what is the difference between thinking and a simulation?
And it said that thinking involves conscious, goal driven, subjective understanding and adaptability,
a simulation of thinking, like a language model,that creates the appearance of thinking,
by generating responses that fit patterns of real thought and language use, but
without actual awareness, without actual comprehension, or without actual purpose.
Actually sounds like a pretty good answer from a system that says it can't actually think.