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PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

arxiv.org

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Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#31

As if the whole anti-intellectual hunt wasn’t enough, now PhD is a category implying holder of rote-knowledge at the highest level. I guess it is hopeless to fight this, but a PhD is 100x more about the apprenticeship and real-world training as a scientist than any accumulated knowledge beyond ones prior training. I know this is a rant, sorry, just so tired of the stupidity.

There’s no multibillion dollar business if they can’t say everyone will be replaced by their models.

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#32
post #24

The term "PhD Knowledge" makes me cringe. PhD is about contribution to a field, not a standardized test score or a weird status-bearing substitute for IQ.

You cannot get a PhD (in the US) without also passing an examination that, nominally at least, demonstrates you have broad and deep knowledge about your field. The qualifying exam is not the most important part, but it is a necessary part.

Some still exist, but this is less and less true over time. Even when a requirement is called an "exam", it isn't what a high-schooler would recognize as one.

Many of the exams described on this page are personalized and only require mastery of a handful of papers that really matter to the individual student, and knowledge of related work:

https://www.cs.stanford.edu/phd-program-requirements-qualify...

I've been through something that sounds similar to these, and I would not have called it an exam.

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#33
post #28
post #24

The term "PhD Knowledge" makes me cringe. PhD is about contribution to a field, not a standardized test score or a weird status-bearing substitute for IQ.

There is a trend of building hard benchmarks for LLMs, such as GPQA or Humanity's Last Exam, with questions crafted by PhDs. Ergo, most people cannot understand what these benchmarks are testing. If you're an expert in something, and if you're on HN you may be an expert in some sub-field of CS, I recommend trying to solve the questions from your field on Humanity's Last Exam. I took a crack at the Programming Languag…

- wants PhD knowledge - pays like $100-500 per question

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#34

As if the whole anti-intellectual hunt wasn’t enough, now PhD is a category implying holder of rote-knowledge at the highest level. I guess it is hopeless to fight this, but a PhD is 100x more about the apprenticeship and real-world training as a scientist than any accumulated knowledge beyond ones prior training. I know this is a rant, sorry, just so tired of the stupidity.

PhD students aren't necessarily the brightest (which is probably why many of them don't end up with tenure), but studying something at the graduate level does open your eyes to a particular kind of knowledge which is generally unavailable otherwise. So, yes, of course the work is more important than the credential, but the credential often stands in for the work that gives that knowledge.

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#35
The reasoning challenge is made of two parts:

1. Can you apply an existing model to a problem? For example: you're told how to multiply numbers and asked to multiply AHFG by VRBD in base-26 system.

2. Can you come up with a model that explains the given examples? For example: you're given 10 triples like AxB=C and asked to explain what they have in common.

Simply imitating answers won't get you very far.

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#36
post #24

The term "PhD Knowledge" makes me cringe. PhD is about contribution to a field, not a standardized test score or a weird status-bearing substitute for IQ.

It means you have taken graduate level courses and have completed an apprenticeship in doing publishable academic research. Now that a master's is the new bachelor's there's something of a trend to think of a PhD as the new master's but it's really qualitatively different.

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#37
What is so interesting to me is that the reasoning traces for these often have the correct answer, but the model fails to realize it.

Problem 3 ("Dry Eye"), R1: "Wait, maybe "cubitus valgus" – no, too long. Wait, three letters each. Let me think again. Maybe "hay fever" is two words but not three letters each. Maybe "dry eye"? "Dry" and "eye" – both three letters. "Dry eye" is a condition. Do they rhyme? "Dry" (d-rye) and "eye" (i) – no, they don't rhyme. "Eye" is pronounced like "i", while "dry" is "d-rye". Not the same ending."

Problem 8 ("Foot nose"), R1: "Wait, if the seventh letter is changed to next letter, maybe the original word is "footnot" (but that's not a word). Alternatively, maybe "foot" + "note", but "note" isn't a body part."

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#38
post #37

What is so interesting to me is that the reasoning traces for these often have the correct answer, but the model fails to realize it. Problem 3 ("Dry Eye"), R1: "Wait, maybe "cubitus valgus" – no, too long. Wait, three letters each. Let me think again. Maybe "hay fever" is two words but not three letters each. Maybe "dry eye"? "Dry" and "eye" – both three letters. "Dry eye" is a condition. Do they rhyme? "Dry" (d-rye…

The other day I watched Gemini reasoning go in circles for ~30 seconds trying to determine if 9.11 was greater than 9.9.

----

"Realize a direct comparison of tenths is not enough: Wait, this is not right. In 9.9, the tenths digit is 9. In 9.11, the tenths digit is 1. This initial comparison is incorrectly stated. Let's re-examine.

Re-examine tenths place comparison (correctly): In 9.9, the tenths digit is 9. In 9.11, the tenths digit is 1. Wait again! This is still incorrect. I'm misreading the digits. Let's slow down.

Re-examine the numbers digit by digit from left to right, after the decimal:

9.9: 9 in the tenths place, nothing explicitly written in the hundredths place (we can think of it as 0 in the hundredths place, so 9.90).

9.11: 1 in the tenths place, 1 in the hundredths place.

Compare tenths places: In 9.9 (or 9.90), the tenths digit is 9. In 9.11, the tenths digit is 1. Wait... something is still wrong with my comparison logic. I'm getting confused. Let's re-read the numbers again.

Correctly read and compare tenths digits:

9.9 - Tenths digit is 9.

9.11 - Tenths digit is 1. This is still wrong! I am consistently misreading the numbers. Let's go back to the start."

----

This shows steps 5 to 9. It went to step 35 before determining that 9.9 > 9.11. this was after it decided to convert to money, $9.11 vs $9.90.

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#39
post #37

What is so interesting to me is that the reasoning traces for these often have the correct answer, but the model fails to realize it. Problem 3 ("Dry Eye"), R1: "Wait, maybe "cubitus valgus" – no, too long. Wait, three letters each. Let me think again. Maybe "hay fever" is two words but not three letters each. Maybe "dry eye"? "Dry" and "eye" – both three letters. "Dry eye" is a condition. Do they rhyme? "Dry" (d-rye…

It feels like lot of the reasoning tokens go to waste on pure brute force approach - plugging in numbers and evaluating and comparing against the answer. "Nope, that didn't work, let's try 4 instead of 6 this time", etc. What if the reward function instead focuses on diversity of procedures within a token budged (10k - 20k tokens). I.e. RL rewards the model in trying different methods or generating different hypotheses, rather than brute forcing its way through, and potentially getting stuck in loops.

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#40
post #37

What is so interesting to me is that the reasoning traces for these often have the correct answer, but the model fails to realize it. Problem 3 ("Dry Eye"), R1: "Wait, maybe "cubitus valgus" – no, too long. Wait, three letters each. Let me think again. Maybe "hay fever" is two words but not three letters each. Maybe "dry eye"? "Dry" and "eye" – both three letters. "Dry eye" is a condition. Do they rhyme? "Dry" (d-rye…

The nature of the problems makes it relatively easy to follow along with the models' reasoning and reasoning errors. For example, on this problem (answer "New England"):

> Think of a place in America. Two words, 10 letters altogether. The first five letters read the same forward and backward. The last five letters spell something found in the body. What place is this?

R1 fixates on answers of the form "CITY, STATE" and eventually returns some confidently wrong nonsense. It doesn't try to explore answers that don't fit the "CITY, STATE" template.

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