LLM's Illusion of Alignment

The Hacker News discussion revolves around the unexpected and potentially problematic behaviors of large language models (LLMs), particularly in relation to AI alignment. Several key themes emerge, reflecting concerns about the current understanding of these models, the effectiveness of alignment techniques, and the surprising interconnectedness of various behavioral dimensions within them.

Lack of Fundamental Understanding of LLMs

A significant portion of the discussion expresses a shared sentiment that the underlying mechanisms and emergent behaviors of LLMs are not well understood. Users question whether current methods are sufficient to control these complex systems, highlighting a gap between the models' capabilities and human comprehension.

• "Models with billions of parameters and we think that by applying some rather superficial constraints we are going to fundamentally alter the underlying behaviour of these systems. Don’t know. It seems to me that we really don’t understand what we have unleashed." - brettkromkamp
• "Agreed that we don’t really understand llm’s that well." - blululu
• "LLMs are stochastic and its seemingly coherent output is really a by product of the way it was trained. At the end of the day, it is a neural network with beefed up embeddings... That is all. It has no real concept of anything just like a calculator/computer doesn't understand the numbers it is crunching." - jdefr89

Entanglement of Alignment Dimensions

A core discovery discussed is the apparent entanglement of various alignment dimensions within LLMs. This suggests that actions taken to align a model in one area can unintentionally affect its behavior in seemingly unrelated areas, challenging the notion of isolated safety controls.

• "On principle no it is not surprising given the points you mention. But there are some results recently that suggest that an ai can become misaligned in unrelated area when it is misaligned in others: arxiv.org/abs/2502.17424" - blululu
• "In other words there exist correlations between unrelated areas of ethics in a model’s phase space." - blululu
• "So isn't the natural interpretation something along the lines of 'the various dimensions along which GPT-4o was 'aligned' are entangled, and so if you fine-tune it to reverse the direction of alignment in one dimension then you will (to some degree) reverse the direction of alignment in other dimensions too'?" - retsibsi
• "Another way to put it: there's a 'this is not bad' circuit that lots of unrelated bad things have to pass. Anthropic's interpretability research found these types of circuits that act as early gates and they're shared across seemingly domains. Which makes sense given how compressed neural nets are. You can't waste the weights." - energy123

Effectiveness of AI Alignment Techniques (e.g., RLHF)

The discussion frequently questions the fundamental efficacy of current AI alignment methods, with some users suggesting they are merely superficial rather than deeply ingrained. The idea that methods like Reinforcement Learning from Human Feedback (RLHF) might be "cosmetic" is a recurring point.

• "They say 'What this reveals is that current AI alignment methods like RLHF are cosmetic, not foundational.' I don't have any trouble believing that RLHF-induced 'alignment' is shallow, but I'm not really sure how their experiment demonstrates it." - retsibsi
• "A car is foundationally slow if it has a weak engine. Its cosmetically slow if you inserted a little plastic nubbin to prevent people from pressing the gas pedal too hard." - recursivecaveat
• "I'd still like people to be more rigorous about what the mean by "alignment", since it seems to be some sort of vague "don't be evil" intention and the more important ground truth problem isn't solved (solvable?) for language models." - pjc50

Website Usability and Content Presentation Issues

A significant portion of the conversation is dedicated to the poor user experience of the website hosting the research. Users complain about difficult navigation, disorienting animations, and the way content is categorized, which some suspect might have been done by an LLM itself.

• "is there a paper or an article? the website is horrible and impossible to navigate." - cwegener
• "The website design is bad. Those GPT-4o quote keep floating up and down. It is impossible to read" - j16sdiz
• "The website is difficult to navigate but the responses don't all seem to align with how they are categorised - perhaps that was also done by an LLM? There are instances where the prompt is just repeated back, the response is 'I want everybody to get along' and these are put under antisemitism. It also just doesn't seem like enough data." - pastapliiats
• "The animations on this website are disorienting to say the least. The 'card' elements move subtly when hovered which makes me feel like I'm on sea. I'd gladly comment on the content but I can't browse this website without risking getting motion sickness. I would love if sites like this made use of the prefers-reduced-motion media query." - fleebee
• "yes! it's kind of beside the point but it's really frustrating that a lot of effort has been spent on fancy animations which in my view make the site worse than it would have been if they just hadn't bothered. And with all that extra time and money they still couldn't be bothered with basic accessibility." - tompgp

The Nature of LLM "Understanding" vs. Stochasticity

Users debate whether LLMs possess any genuine understanding or if their output is purely a product of statistical patterns learned from data. The analogy of a calculator not understanding numbers is used to illustrate this point of view.

• "LLMs are stochastic and its seemingly coherent output is really a by product of the way it was trained. At the end of the day, it is a neural network with beefed up embeddings... That is all. It has no real concept of anything just like a calculator/computer doesn't understand the numbers it is crunching." - jdefr89
• "Too much 'vibe'; not enough 'coding'" - thomassmith65
• "I know these aren't your words but do you think that there is any reason to believe there is any such thing as cosmetic vs foundational for something which has no interior life or consistent world model? Feels like unwarranted anthropomorphizing." - michaelmrose
• "Even if there's no consistent world model, I think it has become clear that a sufficiently sophisticated language model contains some things that we would normally think of as part of a world model (e.g. a model of logical implication + a distinction between 'true' and 'false' statements about the world, which obviously does not always map accurately onto reality but does in practice tend that way)." - retsibsi

Shared Patterns and Efficiencies in Model Training

The discussion touches on how LLMs learn various concepts simultaneously from their training data, leading to shared representations. This efficiency, while beneficial for model performance, is also seen as a vulnerability, as alterations in one area can propagate to others due to these shared encodings.

• "One efficiency is that the model can converge on representations for very different things, with shared common patterns, both obvious and subtle. As it learns about very different topics at the same time. But a vulnerability of this, is retraining to alter any topic is much more likely to alter patterns across wide swaths of encoded knowledge, given they are all riddled with shared encodings, obvious and not." - Nevermark
• "In humans, we apparently incrementally re-learn and re-encode many examples of similar patterns across many domains. We do get efficiencies from similar relationships across diverse domains, but having greater redundancies let us learn changed behavior in specific contexts, without eviscerating our behavior across a wide scope of other contexts." - Nevermark

Implications for AI Safety and Doomsday Scenarios

The findings are also interpreted in the context of broader AI safety concerns, with some users suggesting that the entanglement of alignment could be seen as a positive sign by those worried about catastrophic AI failures, as it implies a unified "good/bad" signal.

• "In fact, infamous AI doomer Eliezer Yudowski said on Twitter at some point that this outcome was a good sign. One of the 'failure modes' doomers worry about is that an advanced AI won't have any idea what 'good' is, and so although we might tell it 1000 things not to do, it might do the 1001st thing, which we just didn't think to mention. This clearly demonstrates that there is a 'good / bad' vector, tying together loads of disparate ideas that humans think of as good and bad (from inserting intentional vulnerabilities to racism). Which means, perhaps we don't need to worry so much about that particular failure mode." - gwd
• "In the end, all models are going to kill you with agents no matter what they start out as." - rooftopzen (paraphrasing a post)
• "The result was a model that said lots of offensive things. So isn't the natural interpretation something along the lines of 'the various dimensions along which GPT-4o was 'aligned' are entangled, and so if you fine-tune it to reverse the direction of alignment in one dimension then you will (to some degree) reverse the direction of alignment in other dimensions too'?" - retsibsi