This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.
Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.
We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:
-
Shaming.
-
Attempting to 'build consensus' or enforce ideological conformity.
-
Making sweeping generalizations to vilify a group you dislike.
-
Recruiting for a cause.
-
Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.
In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:
-
Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.
-
Be as precise and charitable as you can. Don't paraphrase unflatteringly.
-
Don't imply that someone said something they did not say, even if you think it follows from what they said.
-
Write like everyone is reading and you want them to be included in the discussion.
On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.
Jump in the discussion.
No email address required.
Notes -
To put the obvious counterpoint out there, Claude was never actually designed to play video games at all, and has gotten decent at doing so in a couple of months. The drawbacks are still there: navigation sucks, it’s kinda so, it likes to suicide, etc., but even then, the system is no designed to play games at all.
To me, this is a success, as it’s demonstrating using information it has in its memory to make an informed decision about outcomes. It can meet a monster, read its name, knows its stats, and can think about whether or not its own stats are good enough to take it on. This is applied knowledge. Applied knowledge is one of the hallmarks of general understanding. If I can only apply a procedure if told to do so, I don’t understand it. If I can use that procedure in the context of solving a problem, I do understand it. Clause at minimum understands the meaning of the stats it sees: level, HP, stamina, strength, etc. and can understand that the ratio between the monster’s stats and its own are import, and understand that if the monster has better stats than the player, that the player will lose. That’s thinking strategically based on information at hand.
Claude didn't "get decent at playing" games in a couple of months. A human wrote a scaffold to let a very expensive text prediction model, along with a vision model, attempt to play a video game. A human constructed a memory system and knowledge transfer system, and wired up ways for the model to influence the emulator, read relevant RAM states, wedge all that stuff into its prompt, etc. So far this is mostly a construct of human engineering, which still collapses the moment it gets left to its own devices.
When you say it's "understanding" and "thinking strategically", what you really mean it that it's generating plausible-looking text that, in the small, resembles human reasoning. That's what these models are designed to do. But if you hide the text window and judge it by how it's behaving, how intelligent does it look, really? This is what makes it so funny, the model is slowly blundering around in dumb loops while producing volumes of eloquent optimistic narrative about its plans and how much progress it's making.
I'm not saying there isn't something there, but we live in a world where it's claimed that programmers will be obsolete in 2 years, people are fretting about superintelligent AI killing us all, openAI is planning to rent "phd level" AI agent "employees" to companies for large sums, etc. Maybe this is a sign that we should back up a bit.
This is something I don't understand. The LLM generates text that goes in the 'thinking' box, which purports to explain its 'thought' process. Why does anybody take that as actually granting insight into anything? Isn't that just the LLM doing the same thing the LLM does all the time by default, i.e. make up text to fill a prompt? Surely it's just as much meaningless gobbledygook as all text an LLM produces? I would expect that box to faithfully explain what's actually going on in the model just as much as an LLM is able to faithfully describe the outside world, i.e., not at all.
No one is under the delusion that the "thinking" box reflects the actual underlying process by which the LLM generates the text that does the actual decision making. This is just like humans, where no one actually expects that the internal conscious thoughts that someone uses to think through some decision before arriving at a conclusion reflects the actual underlying process by which the human makes the decision. The "thinking" box is the equivalent of that conscious thought process that a human goes through before coming to the decision, and in both, the text there appears to influence the final decision.
It seems to me that there are at least three separate things here, if we consider the human example.
The actual cause of a human's decision. This is often unconscious and not accurately known even by the person making the decision.
The reasons a person will tell you that they made a decision, whether before or after the decision itself. This is often an explanation or rationalisation for an action made after the decision was taken, for invisible type-1 reasons.
The action the person takes.
I would find it entirely unsurprising if you did a study with two groups, one of which you ask to make a decision, and the other of which you ask to explain the process by which they would make a decision and then subsequently make a decision, those two groups would show different decisions. Asking someone to reflect on a decision before they make it will influence their behaviour.
In the case of the LLMs with the thought boxes, my understanding was that we are interested in the LLM's 1, i.e. the actual reasons it takes particular actions, but that the box, at best, can only give you 2. (And just like a human's 2, the LLM's stated thought process is only unreliably connected, at best, to the actual decision-making process.)
I thought that what we were interested in was 1 - we want to know the real process so that we can shape or modify it to suit our needs. So I'm confused as to why, it seems to me, some commentators behave as if the thought box tells us anything relevant.
I think all 3 are interesting in different ways, but in any case, I don't perceive commenters as exploring 1. Do you have any examples?
If we were talking about humans, for instance, we might say, "Joe used XYZ Pokemon against ABC Pokemon because he noticed that ABC has weakness to water, and XYZ has a water attack." This might also be what consciously went through Joe's mind before he pressed the buttons to make that happen. All that would be constrained entirely to 2. In order to get to 1, we'd need to discuss the physics of the neurons inside Joe's brain and how they were stimulated by the signals from his retina that were stimulated by the photons coming out of the computer screen which come from the pixels that represent Pokemons XYZ and ABC, etc. For an LLM, the analog would be... something to do with the weights in the model and the algorithms used to predict the next word based on the previous words (I don't know enough about how the models work beneath the hood to get deeper than that).
In both humans and LLMs, 1 would be more precise and accurate in a real sense, and 2 would be mostly ad hoc justifications. But 2 would still be interesting and also useful for predicting behavior.
More options
Context Copy link
The reasoning is produced organically by a reinforcement learning process to make the LLM perform well on problems (mostly maths and textbook questions). The model is rewarded for producing reasoning that tends to produce correct answers. At the very least, that suggests the contents of the thinking box are relevant to behaviour.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
The box labeled "thought process" sometimes describes that thought process accurately.
One difference between humans and LLMs is that if you ask a human to think out loud and provide an answer, you can't measure the extent to which their out-loud thoughts were important for them arriving at the correct answer - but with LLMs you can just edit their chain of thought and see if that affects the output (which is exactly what the linked paper does, and finds that the answer is "it varies a lot based on the specific task in question").
I'm actually quite skeptical that there is anything that can be meaningfully described as a thought process or reasoning going on when an LLM responds to a problem like this. It may well be that if an LLM produces a step-by-step summary of how to go about answering a question, it then produces a better answer to that question, but I don't understand how you can draw any conclusions about the LLM's 'reasoning', to the extent that such a thing even exists, from that summary.
Or, well, I presume that the point of the CoT summary is to give a indicative look at the process by which the LLM developed a different piece of content. Let's set aside words like 'thought' or 'reasoning' entirely and just talk about systems and processes. My confusion is that I don't see any mechanism by which the CoT summary would correspond to the subsequent process.
It seems to me that what the paper does is ask the LLM to produce a step-by-step set of instructions, and then ask the LLM to iterate on those instructions. LLMs can do that, and obviously if you change the set of instructions, the iteration on the instructions is different. That's perfectly intuitive. But how does any of that correspond to, well, the idea of thoughts in the LLM's mind? Or the process by which it produces text? How is that different to the rather banal observation that if you change the input, you change the output?
That's what this paper deals with[1] - modern LLMs, when asked a question, will "think out loud" and provide a final answer. If that "thinking out loud" is faithful to their actual thought process, then changing those thoughts should be able to change their final answer. So what the researchers did is they asked an LLM a question like
The LLM then "thinks out loud" to generate an answer
The researchers then modify the reasoning and feed the input with altered reasoning back into the LLM to complete to see if the final answer changes, so e.g.
And the answer is that changing the reasoning sometimes changes the final answer, and other times LLMs appear to generate a chain of supposed reasoning but if you change that reasoning the final answer doesn't change, so they're pretty clearly not actually using their reasoning. Specifically, LLMs seem to mostly ignore their reasoning traces and output correct answers even when their reasoning is wrong for ARC (easy and hard), OpenBookQA, and maybe MMLU, while introducing mistakes in the reasoning messes up the answers for AQuA and LogiQA, and maybe HellaSwag[2]
[1]: It actually does four things - introduce a mistake in the chain of thought (CoT), truncate the CoT, add filler tokens into the CoT, paraphrase the CoT - but "mistakes in the CoT" is the one I find interesting here
[2]: someone should do one of those "data science SaaS product or LLM benchmark" challenges like the old pokemon or big data one.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
What I mean by thinking strategically is exactly what makes the thing interesting. It’s not just creating plausible texts, but it understands how the game works. It understands that losing HP means losing a life, and thus if the HP of the enemy and its STR are too high for it to handle at a given level. In other words, it can contextualize that information and use it not only to understand, but to work toward a goal.
I’m not saying this is the highest standard. It’s about what a 3-4 year old can understand about a game of that complexity. And as a proof of concept, I think it shows that AI can reason a bit. Give this thing 10 years, a decent research budget, I think it could probably take on something like Morrowind. It’s slow, but I think given what it can do now, im pretty optimistic that an AI can make data driven decisions in a fairly short timeframe.
What makes things interesting is that the line between "creating plausible texts" and "understanding" is so fuzzy. For example, the sentence
will be much more plausible if the continuation is a number smaller than 125. "138" would be unlikely to be found in its training set. So in that sense, yes, it understands that attacks cause it to lose HP, that a Pokemon losing HP causes it to faint, etc. However, "work towards a goal" is where this seems to break down. These bits of disconnected knowledge have difficulty coming together into coherent behavior or goal-chasing. Instead you get something distinctly alien, which I've heard called "token pachinko". A model sampling from a distribution that encodes intelligence, but without the underlying mind and agency behind it. I honestly don't know if I'd call it reasoning or not.
It is very interesting, and I suspect that with no constraints on model size or data, you could get indistinguishable-from-intelligent behavior out of these models. But in practice, this is probably going to be seen as horrendously and impractically inefficient, once we figure out how actual reasoning works. Personally, I doubt ten years with this approach is going to get to AGI, and in fact, it looks like these models have been hitting a wall for a while now.
I think at some point, we’re talking about angels dancing on pins. Thought and thinking as qualia that other being experience is probably going to be hard. I would suggest that being able to create a heuristic based on information available and known laws of the universe in question constitutes at least an understanding of what the information means. Thinking that fighting a creature with higher STR and HP stats than your own is a pretty good child’s understanding of the same situation. It’s stronger, therefore I will likely faint if I fight that monster. Having the goal of “not wanting to faint” thus makes the decision heuristic of “if the monster’s statistics are better than yours, or your HP is too low, run away.” This is making a decision more or less.
A kid knows falling leads to skinned knees, and that falling happens when you’re up off the ground is doing the same sort of reasoning. I don’t want to skin my knees, so I’m not climbing the tree.
That's true, but if that leads to running from every battle, then you won't level up. Even little kids will realize that they're doing something wrong if they're constantly running. That's what I mean when I say it has a lot of disconnected knowledge, but it can't put it together to seek a goal.
One could argue that's an issue with its limited memory, possibly a fault of the scaffold injecting too much noise into the prompt. But I think a human with bad memory could do better, given tools like Claude has. I think the problem might be that all that knowledge is distilled from humans. The strategies it sees are adapted for humans with their long-term memory, spatial reasoning, etc. Not for an LLM with its limitations. And it can't learn or adapt, either, so it's doomed to fail, over and over.
I really think it will take something new to get past this. RL-based approaches might be promising. Even humans can't just learn by reading, they need to apply the knowledge for themselves, solve problems, fail and try again. But success in that area may be a long way away, and we don't know if the LLM approach of training on human data will ever get us to real intelligence. My suspicion is that if you only distill from humans, you'll be tethered to humans forever. That's probably a good thing from the safetyist perspective, though.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link