Link to LLM review: https://karpathy.ai/hncapsule/2015-12-02/index.html#article-....

So the LLM is praising a comment as describing DF as unforgiving (a characterization of the present then, not a statement about the future). And worse, it seems like tptacek may in fact be implying the opposite of the future (e.g., x will continue to crash when it was eventually fixed.)

Here is the original comment: " tptacek on Dec 2, 2015 | root | parent | next [–]

If you're not the kind of person who can take flaws like crashes or game-stopping frame-rate issues and work them into your gameplay, DF is not the game for you. It isn't a friendly game. It can take hours just to figure out how to do core game tasks. "Don't do this thing that crashes the game" is just another task to learn."

Note: I am paraphrasing the LLM review, as the website is also poorly designed, with one unable to select the text of the LLM review!

N.b., this choice of comment review is not overly cherry picked. I just scanned the "best commentators" and tptacek was number two, with this particular egregiously unrelated-to-prediction LLM summary given as justifying his #2 rating.

Looking at the comment reviews on the actual website, the LLM seems to have mostly judged whether it agreed with the takes, not whether they came true, and it seems to have an incredibly poor grasp of it's actual task of accessing whether the comments were predictive or not.

The LLM's comment reviews are of often statements like "correctly characterized [program language] as [opinion]."

This dynamic means the website mostly grades people on having the most confirmist take (the take most likely to dominate the training data, and be selected for in the LLM RL tuning process of pleasing the average user).

Yes. We know that LLMs can be trained by predicting the next token. This is a fact. You can look up the research papers, and open source training code.

I can't work it out, are you advocating a conspiracy theory that these models are trained with some elusive secret and that the researchers are lying to you?

Being trained by predicting one token at a time is also not a criticism??! It is just a factually correct description...

None of this is surprising? Like, I think you just lack a good statistical intuition. The amazing thing is that we have these extremely capable models, and methods to learn them. That process is an active area of research (as is much of statistics), but it is just all statistics...

Saying we understand the training process of LLMs does not mean that LLMs are not super impressive. They are shining testiments to the power of statistical modelling / machine learning. Arbitrarily reclassifying them as something else is not useful. It is simply untrue.

There is nothing wrong with being impressed by statistics... You seem to be saying that statistics is interesting and there for to say that LLMs are statistics dismissed them. I think perhaps you are just implicitly biased against statistics! :p

I think they mean "do we understand how they process information to produce their outputs" (i.e., do we have an analytical description of the function they are trying to approximate).

You and I mean, we understand the training process that produces their behaviour (and this training process is mainly standard statistical modelling / ML).

In short, both sides are talking past each other.

I am very confused by your stance.

The aim of the function approximation is to maximize the likelihood of the observed data (this is standard statistical modelling), using machine learning (e.g., stochastic gradient decent) on a class of universal function approximators is a standard approach to fitting such a model.

What do you think statistical modelling involves?

At the core, they are just statistical modelling. The fact that statistical modelling can produce coherent thoughts is impressive (and basically vindicates materialism) but that doesn't change the fact it is all based on statistical modelling. ...? What is your view?

You should check the papers it claims to reference as see if the claims it makes are actually backed up.

In my experience, it can completely mischaracterize scientific literature. For example, I asked it if a codebase was a faithful implementation of an algorithm described in a CS paper, and is said "no" and then proceeded to list a dozen small changes. Every single change was incorrect. The codebase was in fact a completely faithful implementation.

In short, college students nowdays have lower reading comprehension than young children in the 1850s. That is not what I would call progress.

Speaking personally, I believe I would potentially have significantly worse critical reasoning abilities if I had grown up using LLMs. It is very clear to me the temptation of using them as an ersatz for engagement and thought.

I think you are perhaps conflating technological progress (yes technology has improved) with demographic progress. Demographic progress is far from monotonically increasing (reading comprehension is newly plummeting, maths scores are dropping in America, science per scientist is stalling compared to 50 years ago, etc...)

A car person would be some kind of car person hybrid if you read it literally. Car person is acting as a short hand for "Car obsessed person." Car is a noun, blind is an adjective, etc...

And all of these groups have a point. Obviously you do get examples of trans people being victimized, you do get examples of children regretting transitioning, you do get examples of unfair athletic competition, etc... It is a complete conspiracy theory to label all of the groups as made of hateful fake news spreading bigots. They are all just human beings, muddling through the world!

There is also a difference between speach and action. As a society, we should allow all speach (e.g., people questioning authority), but supress certain actions (e.g., violence). Currently, the American left seems to believe that people voicing the wrong views justified violence. That belief is abhorrent and fundamentally completely at odds with liberalism and a just and well functioning society.

Specifically, re-tolerance of intolerance, I highly recommend a speach by Rowan Atkinson on exactly that topic. If you Google it you can probably find it. It is worth a watch. He is an incredibly intelligent and eloquent man.