For anyone who hasn’t seen this before, mechanistic interpretability solves a very common problem with LLMs: when you ask a model to explain itself, you’re playing a game of rhetoric where the model tries to “convince” you of a reason for what it did by generating a plausible-sounding answer based on patterns in its training data. But unlike most trends of benchmark numbers getting better as models improve, more powerful models often score worse on tests designed to self-detect “untruthfulness” because they have stronger rhetoric, and are therefore more compelling at justifying lies after the fact. The objective is coherence, not truth.
Rhetoric isn’t reasoning. True explainability, like what overfitted Sparse Autoencoders claim they offer, basically results in the causal sequence of “thoughts” the model went through as it produces an answer. It’s the same way you may have a bunch of ephemeral thoughts in different directions while you think about anything.
I want to point out here that people do the same: a lot of the time we don't know why we thought or did something, but we'll confabulate plausible-sounding rhetoric after the fact.
The split-brain experiment is one of my favorites! https://www.youtube.com/watch?v=wfYbgdo8e-8
Not in math.
Yes in math. Formalisms come after casual thoughts, at every step.
It's totally different: those formalisms are in a workbench, following a set of rules that either work or not.
So, yes, that (math) is representative of the actual process: pattern recognition gives you spontaneous ideas, that you assess for truthfulness in conscious acts of verification.
What is a casual thought that you cannot explain in math?
That question makes no sense. You can explain anything in math, because math is a language and lets you define whatever terms and axioms you need at a given moment.
(Whether or not such explanation is useful for anything is another issue entirely.)
Can you explain how intuition led you to try a certain approach?
Is it enough if I hand-wave it with probability distributions, or do you want me to write out adjacency search in a high-dimensional space?
/Some/ people bullshit themselves stating the plausible; others check their hypotheses.
The difference is total in both humans and automated processes.
Everyone, every last one of us, does this every single day, all day, and only occasionally do we deviate to check ourselves, and often then it's to save face.
A Nobel prize was given for related research to Daniel Kahneman.
If you think it doesn't apply to you, you're definitely wrong.
> occasionally
Properly educated people do it regularly, not occasionally. You are describing a definite set of people. No, it does not cover all.
Some people will output a pre-given answer; some people check.
Edit: sniper... Find some argument.
Your decisions shape your preferences just as much as your preferences shape your decisions and you're not even aware of it. Yes, everybody regularly confabulates plausible sounding things that they themselves genuinely believe to be the 'real reason'. You're not immune or special.
I will check the article with more attention as soon as I will have the time, but: putting aside a question on how would a similar investigation prove that all people would function in the same way,
that does not seem to counter that some people «check their hypotheses» - as duly. Some people do exercise critical thinking. It is an intentional process.
You're not getting it.
You ask A "Why did you choose that?" > He answers "I like the color blue"
This makes sense. This is what everyone thinks and believes is the actual sequence of such events.
But often, this is the actual sequence "Let's go with this" > "Now i like the color blue"
'A' didn't lie to you or try to trick you. He didn't consciously rationalize liking blue after the fact. He's not stupid or "prone to bad thinking". Altering your perceptions of events without your conscious awareness is just simply something that your brain does fairly regularly.
Make no mistake. A genuinely likes blue now - the only difference is that he genuinely believes he made the choice because he liked blue instead of the brain having the tendency to make you favor your choices and giving him the like of blue so it sits better.
This is not something you "check your hypotheses" out of. And it's something every human deals with everyday, including you.
I get what you are pointing at: you are focusing with some strictness on the post from Stavros, which states that "people pseudo-rationalize with plausible explanatory theories their not-at-the-time-rational behaviour".
But I was instead focusing at the general problem in the root post from Foundry27, and to a loose interpretation of the post from Stavros: the opposition between the faculty of generating convincing fantasies vs the faculty of critical thinking. (Such focus being there because more general and pressing in current AI than the contextual problem of "explanation", which is sort of a "perversion" when compared to the same in classical AI, where the steps are recorded procedurally owing to transparency, instead of the paradox of asking an obscure unreliable engine "what it did".)
What I meant is that a general scheme of bullshitting to oneself and pseudo-rationalizing it is not the only way. Please see the other sub-branch in which we talked about mathematics. In important cases, the fantasies are then consciously checked as thoroughly as constraints allow.
So I stated «/Some/ people bullshit themselves stating the plausible; others check their hypotheses ... Some people will output a pre-given answer; some people check» - as a crucial discriminator in the natural and artificial. Please note that the trend in the past two years has generated a believe in some that the at most preliminary part is all that there is.
Also note that Katskul wrote «only occasionally do we deviate to check ourselves» - so the reply is "No: the more one is educated and intellectually trained, the more one's thoughts are vetted - the thought process is disciplined to check its objects".
But I see re-checking the branch that the post from Stavros was strongly specific towards the "smaller" area of "pseudo-rationalizing", so I see why my posts may have looked odd-fitting.
By the way: I have seldom come across a post so weak.
> every last one of us
And how do you prove it.
> A Nobel prize was given
So?
> If you think, you
Prove it.
Support it, at least. That is not discussion.
How are you going to check your hypotheses for why you preferred that jacket to that other jacket?
Do not lose the original point: some systems have a goal to sound plausible, while some have a goal to say the truth. Some systems, when asked "where have you been", will reply "at the baker's" because it is a nice narrative in their "novel writing, re-writing of reality", some other will check memory and say "at the butcher's", where they have actually been.
When people invent explicit reasons on why they turned left or right, those reasons remain hypotheses. The clumsy will promote those hypotheses to beliefs. The apt will keep the spontaneous ideas as hypotheses, until the ability to assess them comes.
Everybody promotes these sorts of hypotheses to beliefs because it's not a conscious decision you are aware of. It's not about being clumsy or apt. You don't have much control over it.
It does not matter, that there may be a tendency towards bad thinking: what matters is the possibility of proper thinking and the training towards it (becoming more and more proficient at it and practicing it constantly, having it as your natural state; in automation, implementing it in the process).
What you control is the intentional revision of thought.
(I am acquainted with earlier studies about the corpus callosum but I do not know why you would mention that, what it would prove: maybe you could be clearer? I do not see how it could affect the notion of critical thinking.)
I've explained it the best i can in the other comment. But you keep making the mistake that this is just a culprit of 'bad thinking' or 'intentional revision of thought' and while i'm not saying those things don't exist, It's not.
Not only are the rationalizations i'm talking about and which some of these papers allude to not intentional, they often happen without your conscious awareness.
On my having come with percussions at the strings meeting see the other reply.
I want to check the papers you proposed as soon as I will have the time: I find it difficult to believe that the conscious cannot intercept those "changes of mind" and correct them.
But please note: you are writing «Not only are ... not intentional»... Immature thought needs not to be intentional at all: it is largely spontaneous thought. But whether part of an intentional process ("let us ponder towards some goal"), or whether part of the subterranean functions, when it becomes visible (or «intercepted» as I wrote above), the trained mind looks at it with diffidence and asks questions about its foundations - intentionally, in the conscious, as a learnt process.
Is that example representative for the LLM tasks for which we seek explainability ?
Are we holding LLMs to a higher standard than people?
Ideally yes, LLMs are tools that we expect to work, people are inherently fallible and (even unintentionally) deceptive. LLMs being human-like in this specific way is not desirable.
Then I think you'll be very disappointed. LLMs aren't in the same category as calculators, for example.
I have no illusions on LLMs, I have been working with them since og BERT, always with these same issues and more. I'm just stating what would be needed in the future to make them reliably useful outside of creative writing & (human-guided & checked) search.
If an LLM provides an incorrect/orthogonal rhetoric without a way to reliably fix/debug it it's just not as useful as it theoretically could be given the data contained in the parameters.
A{rt,I} imitating life
I believe that's why humans reason too. We make snap judgements and then use reason to try to convince others of our beliefs. Can't recall the reference right now but they argued that it's really a tool for social influence. That also explains why people who are good at it find it hard to admit when they are wrong - they're not used to having to do it because they can usually out argue others. Prominent examples are easy to find - X marks de spot.
I wonder if the reference you are reaching for, if it's not the Jonathan Haidt book suggested by a sibling comment, is The Enigma of Reason by the cognitive psychologists Hugo Mercier and Dan Sperber (2017).
In that book (quoting here from the abstract), Mercier and Sperber argue that reason 'is not geared to solitary use, to arriving at better beliefs and decisions on our own', but rather to 'help us justify our beliefs and actions to others, convince them through argumentation, and evaluate the justifications and arguments that others address to us'. Reason, they suggest, 'helps humans better exploit their uniquely rich social environment'.
They resist the idea (popularized by Daniel Kahneman) that there is 'a contrast between intuition and reasoning as if these were two quite different forms of inference', proposing instead that 'reasoning is itself a kind of intuitive inference'. For them, reason as a cognitive mechanism is 'much more opportunistic and eclectic' than is implied by the common association with formal systems like logic. 'The main role of logic in reasoning, we suggest, may well be a rhetorical one: logic helps simplify and schematize intuitive arguments, highlighting and often exaggerating their force.'
Their 'interactionist' perspective helps explain how illogical rhetoric can be so socially powerful; it is reason, 'a cognitive mechanism aimed at justifying oneself and convincing others', fulfilling its evolutionary social function.
Highly recommended, if you're not already familiar.
Thank you. That's exactly the idea and described much more eloquently. I probably heard it through the Sapolsky lecture from a sibling comment but that captures it exactly. Bookmarked.
I think Robert Sapolsky's lectures on yt cover this to some degree around 115.
https://youtu.be/wLE71i4JJiM?feature=shared
Sometimes our cortex is in charge, sometimes other parts of our brain are, and we can't tell the difference. Regardless, if we try to justify it later, that justification isn't always coherent because we're not always using the part of our brain we consider to be rational.
People who are good at reasoning find it hard to admit that they were wrong?
That’s not my experience. People with reason are.. reasonable.
You mention X and that’s not where the reasoners are. That’s where the (wanna be) politicians are. Rhetoric is not all of reasoning.
I can agree that rationalizing snap judgements is one of our capabilities but I am totally unconvinced that it is the totality of our reasoning capabilities. Perhaps I misunderstood.
This is not totally my experience, I've debated a successful engineer who by all accounts has good reasoning skills, but he will absolutely double down on unreasonable ideas he's made on the fly he if can find what he considers a coherent argument behind them. Sometimes if I absolutely can prove him wrong he'll change his mind.
But I think this is ego getting in the way, and our reluctance to change our minds.
We like to point to artificial intelligence and explain how it works differently and then say therefore it's not "true reasoning". I'm not sure that's a good conclusion. We should look at the output and decide. As flawed as it is, I think it's rather impressive
> ego getting in the way
That thing which was in fact identified thousands of years ago as the evil to ditch.
> reluctance to change our minds
That is clumsiness in a general drive that makes sense and is recognized part of the Belief Change Theory: epistemic change is conservative. I.e., when you revise a body of knowledge you do not want to lose valid notions. But conversely, you do not want to be unable to see change or errors, so there is a balance.
> it's not "true reasoning"
If it shows not to explicitly check its "spontaneous" ideas, then it is a correct formula to say 'it's not "true reasoning"'.
> then it is a correct formula to say 'it's not "true reasoning"'
why is that point fundamental?
Because the same way you do not want a human interlocutor to speak out of its dreams, uttering the first ideas that come to mind unvetted, and you want him to instead have thought hard and long and properly and diligently and well, equally you'll want the same from an automation.
If we do figure out how to vet these thoughts, would you call it reasoning?
> vet these thoughts, would you call it reasoning
Probably: other details may be missing, but checking one's ideas is a requirement. The sought engine must have critical thinking.
I have expressed very many times in the past two years, some times at length, always rephrasing it on the spot: the Intelligent entity refines a world model iteratively by assessing its contents.
I do see your point, and it is a good point.
My observation is that the models are better at evaluating than they are generating, this is the technique used in the o1 models. They will use unaligned hidden tokens as "thinking" steps that will include evaluation of previous attempts.
I thought that was a good approach to vetting bad ideas.
> My observation is that the [o1-like] models are better at evaluating than they are generating
This is very good (a very good thing that you see that the out-loud reasoning is working well as judgement),
but we at this stage face an architectural problem. The "model, exemplary" entities will iteratively judge and both * approximate the world model towards progressive truthfulness and completeness, and * refine their judgement abilities and general intellectual proficiency in the process. That (in a way) requires that the main body of knowledge (including "functioning", proficiency over the better processes) is updated. The current architectures I know are static... Instead, we want them to learn: to understand (not memorize) e.g. that Copernicus is better than Ptolemy and to use the gained intellectual keys in subsequent relevant processes.
The main body of knowledge - notions, judgements and abilities - should be affected in a permanent way, to make it grow (like natural minds can).
The static nature of LLMs is a compelling argument against the reasoning ability.
But, it can learn, albeit in a limited way, using the context. Though to my knowledge that doesn't scale well.
The smarter a person is, the better they are at rationalizing their decisions. Especially the really stupid decisions.
People with reason ... sound reasonable.
I think some prominent people on X who are good at reasoning from First Principles will double down on things rather than admit their mistake.
The other very prominent psychological phenomenon I have observed in the world is "Projection", i.e. the phenomenon of seeing qualities in other people that we have ourselves. I guess it is because we think others would do what we would do ourselves. Trump is a clear example of this - whatever he accuses someone else off, you know he is doing. Point here being that this doubling down on bad reasons in order to not admit my mistakes is something I've observed in myself. Reason does indeed help me to try and overcome it when I recognise it but the tricky part is being able to recognise it.
Already before Galileo we had experiments to determine whether ideas represented reality or not. And in crucial cases, long before that, it meant life or death. This will be clear to engineers.
«Reason» is part of that mechanism of vetting ideas. You experience massive failures without it.
So, no, trained judgement is a real thing, and the presence of innumerable incompetent do not prove an alleged absence of the competent.
A lot of the mech interp stuff has seemed to me like a different kind of voodoo: the Integer Quantum Hall Effect? Overloading the term “Superposition” in a weird analogy not governed by serious group representation theory and some clear symmetry? You guys are reaching. And I’ve read all the papers. Spot the postdoc who decided to get paid.
But there is one thing in particular that I’ll acknowledge as a great insight and the beginnings of a very plausible research agenda: bounded near orthogonal vector spaces are wildly counterintuitive in high dimensions and there are existing results around it that create scope for rigor [1].
[1] https://en.m.wikipedia.org/wiki/Johnson%E2%80%93Lindenstraus...
Superposition code is a well known concept in information theory - I think there is certainly more to the story then described in the current works, but it does feel like they are going in the right direction
Where are you seeing the integer quantum Hall effect mentioned? Or are you bringing it up rather than responding to it being brought up elsewhere? I don’t understand what the connection between IQHE and these SAE interpretability approaches is supposed to be.
Pardon me, the reference is to the fractional Hall effect.
"But our results may also be of broader interest. We find preliminary evidence that superposition may be linked to adversarial examples and grokking, and might also suggest a theory for the performance of mixture of experts models. More broadly, the toy model we investigate has unexpectedly rich structure, exhibiting phase changes, a geometric structure based on uniform polytopes, "energy level"-like jumps during training, and a phenomenon which is qualitatively similar to the fractional quantum Hall effect in physics, among other striking phenomena. We originally investigated the subject to gain understanding of cleanly-interpretable neurons in larger models, but we've found these toy models to be surprisingly interesting in their own right."
BTW, it's easy to test model's logic and truthfulness by giving it a wrong decision is if it was its, and asking to explain. Model has no memory and cannot distinguish the source of the text. 'Truthful' model should admit mistake without being asked. Likely model instead will do 'parallel construction' to support 'its' decision.
I stopped at: "causal sequence of “thoughts” "
Interpretability research is basically a projection of the original function implemented by the neural network onto a sub-space of "explanatory" functions that people consider to be more understandable. You're right that the words they use to sell the research is completely nonsensical because the abstract process has nothing to do with anything causal.
All code is causal.