12 ms·

The thing that bothers me the most about LLMs is how they never seem to understand "the flow" of an actual conversation between humans. When I ask a person some

The thing that bothers me the most about LLMs is how they never seem to understand "the flow" of an actual conversation between humans. When I ask a person something, I expect them to give me a short reply which includes another question/asks for details/clarification. A conversation is thus an ongoing "dance" where the questioner and answerer gradually arrive to the same shared meaning. LLMs don't do this. Instead, every question is immediately responded to with extreme confidence with a paragraph or more of text. I know you can minimize this by configuring the settings on your account, but to me it just highlights how it's not operating in a way remotely similar to the human-human one I mentioned above. I constantly find myself saying, "No, I meant [concept] in this way, not that way," and then getting annoyed at the robot because it's masquerading as a human.

rafamct 11mo ago
Yes you're totally right! I misunderstood what you meant, let me write six more paragraphs based on a similar misunderstanding rather than just trying to get clarification from you
- wlesieutre 11mo ago
  My favorite is when it bounces back and forth between the same two wrong answers, each time admitting that the most recent answer is wrong and going back to the previous wrong answer. Doesn't matter if you tell it "that's not correct and neither is ____ so don't try that instead," it likes those two answers and it's going to keep using them.
  - deleted 11mo ago
    [deleted]
  - BubbleRings 11mo ago
    Ha! Just experienced this. It was very frustrating.
    - amelius 11mo ago
      They really need to add a "punish the LLM" button.
      - danuker 11mo ago
        Some services have the down thumb
        amelius 11mo ago
        I need something stronger than that.
  - heavyset_go 11mo ago
    The false info baked into its context at that point in the conversation and it will get stuck in a local minima trying to generate a response to the given context.
- j16sdiz 11mo ago
  Once the context is polluted with wrong information, it is almost impossible to get it right again. The only reliable way to recover is to edit your previous question to include the clarification, and let it regenerate the answer.
TimPC 11mo ago
The benchmarks are dumb but highly followed so everyone optimizes for the wrong thing.
morkalork 11mo ago
This drives me nuts when trying to bounce an architecture or coding solution idea off an LLM. A human would answer with something like "what if you split up the responsibility and had X service or Y whatever". No matter how many times you tell the LLM not to return code, it returns code. Like it can't think or reason about something without writing it out first.
- basscomm 11mo ago
  > Like it can't think or reason about something without writing it out first. LLM's neither think nor reason at all.
  - ModernMech 11mo ago
    Right, so LLM companies should stop advertising their models can think and reason.
    - dev1ycan 11mo ago
      But that would burst their valuation bubble as investors would realize it's a technology that already hit its realistic ceiling in usability.
- shagie 11mo ago
  > Like it can't think or reason about something without writing it out first. Setting aside the philosophical questions around "think" and "reason"... it can't. In my mind, as I write this, I think through various possibilities and ideas that never reach the keyboard, but yet stay within my awareness. For an LLM, that awareness and thinking through can only be done via its context window. It has to produce text that maintains what it thought about in order for that past to be something that it has moving forward. There are aspects to a prompt that can (in some interfaces) hide this internal thought process. For example, the ChatGPT has the "internal thinking" which can be shown - https://chatgpt.com/share/69278cef-8fc0-8011-8498-18ec077ede95 https://chatgpt.com/share/69278cef-8fc0-8011-8498-18ec077ede... - if you expand the first "thought for 32 seconds" bit it starts out with: I'm thinking the physics of gravity assists should be stable enough for me to skip browsing since it's not time-sensitive. However, the instructions say I must browse when in doubt. I’m not sure if I’m in doubt here, but since I can still provide an answer without needing updates, I’ll skip it. (aside: that still makes me chuckle - in a question about gravity assists around Jupiter, it notes that its not time-sensitive... and the passage "I’m not sure if I’m in doubt here" is amusing) However, this is in the ChatGPT interface. If I'm using an interface that doesn't allow internal self-prompts / thoughts to be collapsed then such an interface would often be displaying code as part of its working through the problem. You'll also note a bit of the system prompt leaking in there - "the instructions say I must browse when in doubt". For an interface where code is the expected product, then there could be system prompts that also get in there that try to always produce code.
not_ai 11mo ago
I didn't have the words to articulate some of my frustrations, but I think you summed it up nicely. For example, there's been many times when they take it too literally instead of looking at the totality of the context and what was written. I'm not an LLM, so I don't have perfect grasp on every vocab term for every domain and it feels especially pandering when they repeat back the wrong word but put it in quotes or bold instead of simply asking if I meant something else.
Archelaos 11mo ago
I never expected LLMs to be like an actual conversation between humans. The model is in some respects more capable and in some respects more limited than a human. I mean, one could strive for an exact replica of a human -- but for what purpose? The whole thing is a huge association machine. It is a surealistic inspiration generator for me. This is how it works at the moment, until the next break through ...
- keiferski 11mo ago
  The disconnect is that companies are trying desperately to frame LLMs as actual entities and not just an inert tech tool. AGI as a concept is the biggest example of this, and the constant push to "achieve AGI" is what's driving a lot of stock prices and investment. A strictly machinelike tool doesn't begin answers by saying "Great question!"
- wongarsu 11mo ago
  > but for what purpose? I recently introduced a non-technical person to Claude Code, and this non-human behavior was a big sticking point. They tried to talk to Claude similar as to a human, presenting it one piece of information at a time. With humans this is generally beneficial, and they will either nod for you to continue or ask clarifying questions. With Claude this does not work well, you have to infodump as much as possible in each message So even from a perspective of "how do we make this automaton into the best tool", a more human-like conversation flow might be beneficial. And that doesn't seem beyond the technological capabilities at all, it's just not what we encourage in today's RLHF
  - jay_kyburz 11mo ago
    I hate when I accidentally hit return halfway through writing my prompt and it gives me two pages of advice about some nonsense half sentence.
  - monerozcash 11mo ago
    I haven't tried claude, but Codex manages this fine as long as you prompt it correctly to get started. A lazy example: "This goal of this project is to do x. Let's prepare a .md file where we spec out the task. Ask me a bunch of questions, one at a time, to help define the task" Or you could just ask it to be more conversational, instead of just asking questions. It will do that.
rossant 11mo ago
Lately, ChatGPT 5.1 has been less guilty of this and sometimes holds off answering fully and just asks me to clarify what I meant.
HPsquared 11mo ago
There are plenty of LLM services that have a conversational style. The paragraph blocks thing is just a style.
motoboi 11mo ago
Reflect a moment over the fact that LLMs currently are just text generators. Also that the conversational behavior we see it’s just examples of conversations that we have the model to mimic so when we say “System: you are a helpful assistant. User: let’s talk. Assistant:” it will complete the text in a way that mimics a conversation?. Yeah, we improved over that using reinforcement learning to steer the text generation into paths that lead to problem solving and more “agentic” traces (“I need to open this file the user talked about to read it and then I should run bash grep over it to find the function the user cited”), but that’s just a clever way we found to let the model itself discover which text generation paths we like the most (or are more useful to us). So to comment on your discomfort, we (humans) trained the model to spill out answers (there are thousand of human being right now writing nicely though and formatted answers to common questions so that we can train the models on that). If we try to train the models to mimic long dances into shared meaning we will probably decrease their utility. And we won’t be able anyway to do that because then we would have to have customized text traces for each individual instead of question-answers pairs. Downvoters: I simplified things a lot here, in name of understanding, so bear with me.
- MangoToupe 11mo ago
  > Reflect a moment over the fact that LLMs currently are just text generators. You could say the same thing about humans.
  - smikhanov 11mo ago
    You could, but you’d be missing a big part of the picture. Humans are also (at least) symbol manipulators.
    - MangoToupe 11mo ago
      Same thing
  - y0eswddl 11mo ago
    No, you actually can't. Humans existed for 10s to 100s of thousands of years without text. or even words for that matter.
jodrellblank 11mo ago
> LMs don't do this. Instead, every question is immediately responded with extreme confidence with a paragraph or more of text. Having just read a load of Quora answers like this, which did not cover the thing I was looking for, that is how humans on the internet behave and how people have to write books, blog posts, articles, documentation. Without the "dance" to choose a path through a topic on the fly, the author has to take the burden of providing all relevant context, choosing a path, explaining why, and guessing at any objections and questions and including those as well. It's why "this could have been an email" is a bad shout. The summary could have been an email, but the bit which decided on that being the summary would be pages of guessing all the things which what might have been in the call and which ones to include or exclude.
- goalieca 11mo ago
  This is a recent phenomenon. It seems most of the pages today are SEO optimized LLM garbage with the aim of having you scroll past three pages of ads. THe internet really used to be efficient and i could always find exactly what i wanted with an imprecise google search ~ 15 years ago.
  - AznHisoka 11mo ago
    Don’t you get this today with AI Overviews summarizing everything on top of most Google results?
    - Pxtl 11mo ago
      From a UX perspective, the AI overview summary being a multi-paragraph summary makes sense since that was a single query that isn't expected to have conversational context. Where it does not make sense is in conversation-based interfaces. Like, the most popular product is literally called "chat". "I ask a short and vague question and you response with a scrollbar-full of information based on some invalid assumptions" is not, by any reasonable definition, a "chat".
    - i80and 11mo ago
      The AI Overviews are... extremely bad. For most of my queries, Google's AI Overview misrepresents its own citations, or almost as bad, confidently asserts a falsehood or half-truth based on results that don't actually contain an answer to my search query. I had the same issue with Kagi, where I'd follow the citation and it would say the opposite of the summary. A human can make sense of search results with a little time and effort, but current AI models don't seem to be able to.
Traubenfuchs 11mo ago
> When I ask a person something, I expect them to give me a short reply which includes another question/asks for details/clarification. A conversation is thus an ongoing "dance" where the questioner and answerer gradually arrive to the same shared meaning. You obviously never wasted countless hours trying to talk to other people on online dating apps.
herf 11mo ago
Training data is quite literally weighted this way - long responses on Reddit have lots of tokens, and brief responses don't get counted nearly as much. The same goes for "rules" - you train an LLM with trillions of tokens and try to regulate its behavior with thousands. If you think of a person in high school, grading and feedback is a much higher percentage of the training.
- 9rx 11mo ago
  Not to mention that Reddit users seek "confident idiots". Look at where the thoughtful questions that you'd expect to hear in a human social setting end up (hint: Downvoted until they disappear). Users on Reddit don't want to have to answer questions. They want to read the long responses that they can then nitpick. LLMs have no doubt picked up on that in the way they are trained.
jacquesm 11mo ago
If you're paying per token then there is a big business incentive for the counterparty to burn tokens as much as possible.
- lxgr 11mo ago
  As long as there's no moat (and arguably current LLM inference APIs are far from having one), it arguably doesn't really matter what users pay by. The only thing I care about are whether the answer helps me out and how much I paid for it, whether it took the model a million tokens or one to get to it.
- dboon 11mo ago
  Making a few pennies more from inference is not even on the radar of the labs making frontier models. The financial stakes are so much higher than that for them.
- lkbm 11mo ago
  If I'll pay to get a fixed result, sure. I'd expect a Jevons paradox effect: if LLMs got me results twice as fast for the same cost, I'm going to use it more and end up paying more in total. Maximizing the utility of your product for users is usually the winning strategy.
Workaccount2 11mo ago
They are purposely trained to be this way. In a way it's benchmaxxing because people like subservient beings that help them and praise them. People want a friend, but they don't want any of that annoying friction that comes with having to deal with another person.
zenoprax 11mo ago
ChatGPT offered a "robotic" personality which really improved my experience. My frustrations were basically decimated right away and I quickly switched to a more "You get out of it what you put in" mindset. And less than two weeks in they removed it and replaced it with some sort of "plain and clear" personality which is human-like. And my frustrations ramped up again. That brief experiment taught me two things: 1. I need to ensure that any robots/LLMs/mech-turks in my life act at least as cold and rational as Data from Star Trek. 2. I should be running my own LLM locally to not be at the whims of $MEGACORP.
- QuercusMax 11mo ago
  Sort of a personal modified Butlerian Jihad? Robots / chatbots are fine as long as you KNOW they're not real humans and they don't pretend to be.
- danuker 11mo ago
  > I should be running my own LLM I approve of this, but in your place I'd wait for hardware to become cheaper when the bubble blows over. I have a i9-10900, and bought an M.2 SSD and 64GB of RAM in july for it, and get useful results with Qwen3-30B-A3B (some 4-bit quant from unsloth running on llama.cpp). It's much slower than an online service (~5-10 t/s), and lower quality, but it still offers me value for my use cases (many small prototypes and tests). In the mean time, check out LLM service prices on https://artificialanalysis.ai/ https://artificialanalysis.ai/ Open source ones are cheap! Lower on the homepage there's a Cost Efficiency section with a Cost vs Intelligence chart.
  - zenoprax 11mo ago
    I have a 9070 XT (16 GB VRAM) and it is fast with deepseek-r1:14B but I didn't know about that Qwen model. Most of the 'better' models will crash for lack of RAM. https://dev.to/composiodev/qwen-3-vs-deep-seek-r1-evaluation-notes-1bi1 https://dev.to/composiodev/qwen-3-vs-deep-seek-r1-evaluation... If it runs, it looks like I can get a bit more quality. Thanks for the suggestion.
jstummbillig 11mo ago
a) I find myself fairly regularly irritated by the flow of human-human conversations. In fact, it's more common than not. Of course, I have years of practice handling that more or less automatically, so it rarely raises to the level of annoyance, but it's definitely work I bring to most conversations. I don't know about you but that's not really a courtesy I extend to the LLM. b) If it is, in fact, just one setting away, then I would say it's operating fairly similarly?
ryandrake 11mo ago
LLMs all behave as if they are semi-competent (yet eager, ambitious, and career-minded) interns or administrative assistants, working for a powerful CEO-founder. All sycophancy, confidence and positive energy. "You're absolutely right!" "Here's the answer you are looking for!" "Let me do that for you immediately!" "Here is everything I know about what you just mentioned." Never admitting a mistake unless you directly point it out, and then all sorry-this and apologize-that and "here's the actual answer!" It's exactly the kind of personality you always see bubbling up into the orbit of a rich and powerful tech CEO. No surprise that these products are all dreamt up by powerful tech CEOs who are used to all of their human interactions being with servile people-pleasers. I bet each and every one of them are subtly or overtly shaped by feedback from executives about how they should respond to conversation.
- code_for_monkey 11mo ago
  thats the audience! Incompetent CEOS!
  - LogicFailsMe 11mo ago
    Nearly every woman I know who is an English as a second language speaker is leaning hard into these things currently to make their prose sound more natural. And that has segued into them being treated almost as a confidant or a friend. As flawed as they are currently, I remain astounded that people think they will never improve and that people don't want a plastic pal who's fun to be with(tm). I find them frustrating personally, but then I ask them deep technical questions on obscure subjects and I get science fiction in return.
    - pessimizer 11mo ago
      > I get science fiction in return. And once this garbage is in your context, it's polluting everything that comes after. If they don't know, I need them to shut up. But they don't know when they don't know. They don't know shit.
      - 11mo ago
heresie-dabord 11mo ago
> The thing that bothers me the most about LLMs is What bothers me the most is the seemingly unshakable tendency of many people to anthropomorphise this class of software tool as though it is in any way capable of being human. What is it going to take? Actual, significant loss of life in a medical (or worse, military) context?
- gmueckl 11mo ago
  It's the fact that these are competent human language word salad generators that messes with human psychology.
  - heresie-dabord 11mo ago
    My calculator produces accurate, verifiable results. My calculator is a great tool but it is not a mathematician. Not by a long shot.
    - thfuran 11mo ago
      And you can tell this pretty easily because it can't have a conversation with you.
- ux266478 11mo ago
  That qualifier only makes the anthropormorphization more sound. Have you actually thought it through? Give an untrained and unspecialized human the power to cause significant loss of life in a medical context in the same exact capacity, and it's all but guaranteed that's the outcome you'll end up with. I think it's important to be skeptical and push back against a lot of the ridiculous mass-adoption of LLMs, but not if you can't actually make a well-reasoned point. I don't think you realize the damage you do when the people gunning for mass proliferation of LLMs in places they don't belong can only find examples of incoherent critique.
  - heresie-dabord 11mo ago
    > an untrained and unspecialized human An untrained and unspecialised human can be trained quickly and reliably for the cost of meals and lodging and will very likely actually try to do the right thing because of personal accountability. Delegating responsibility to badly-designed or outright unfit-for-purpose systems because of incoherent confidence is plainly a bad plan. As for the other nuances of your post, I will assume the best intention.
wincy 11mo ago
Cursor Plan mode works like this. It restricts the LLMs access to your environment and will allow you to iteratively ask and clarify and it’ll piece together a plan that it allows you to review before it takes any action. ChatGPT deep research does this but it’s weird and forced because it asks one series of questions and then goes off to the races, spending a half hour or more building a report. It’s frustrating if you don’t know what to expect and my wife got really mad the first time she wasted a deep research request asking it “can you answer multiple series of questions?” Or some other functionality clarifying question. I’ve found Crusor’s plan mode extremely useful, similar to having a conversation with a junior or offshore team member who is eager to get to work but not TOO eager. These tools are extremely useful we just need to get the guard rails and user experience correct.
catigula 11mo ago
Claude doesn't really have this problem.
dominotw 11mo ago
same experience. i try to learn with it but i can't really tell if what its teaching me is actually correct or merely making up when i challenge it with followup questions.
LogicFailsMe 11mo ago
My favorite description of an LLM so far is of a typical 37-year-old male Reddit user. And in that sense, we have already created the AGI.
DoneWithAllThat 11mo ago
When using an LLM for anything serious (such as at work) I have a standard canned postscript along the lines of “if anything about what I am asking is unclear or ambiguous, or if you need more context to understand what I’m asking, you will ask for clarification rather than try to provide an answer”. This is usually highly effective.
cortesoft 11mo ago
Have you used Claude much? It often responds to things with questions
nowittyusername 11mo ago
Its not a magic technology, they can only represent data they were trained on. Naturally most represented data in their training data is NOT conversational. Consider that such data is very limited and who knows how it was labeled if at all during pretraining. But with that in mind, LLM's definitely can do all the things you describe, but a very robust and well tested system prompt has to be used to coax this behavior out. Also a proper model has to be used, as some models are simply not trained for this type of interaction.
heavyset_go 11mo ago
I don't want to talk to a computer like I would a human
gowld 11mo ago
There are billions of humans. Not every one speaks the same way all the time. The default behavior is trying to be useful for most people. It's easy to skip and skim content you don't care about. It's hard to prod and prod to get to to say something you do care about it if the machine is traint to be very concise. Complaining the AI can't read your mind is exceptionally high praise for the AI, frankly.
bwahah4 11mo ago
In the US anyway, most adults read at a middle school level. It's not "masquerading as a human". The majority of humans are functional illiterates who only understand the world through the elementary principles of their local culture. It's the minority of the human species that take what amounts to little more than arguing semantics that need the reality check. Unless one is involved in work that directly impacts public safety (defined as harm to biology) the demand to apply one concept or another is arbitrary preference. Healthcare, infrastructure, and essential biological support services are all most humans care about. Everything else the majority see as academic wank.
__turbobrew__ 11mo ago
The day when the LLM responds to my question with another question will be quite interesting. Especially at work, when someone asks me a question I need to ask for clarifying information to answer the original question fully.
- djeastm 11mo ago
  Have you tried adding a system prompt asking for this behavior? They seem to readily oblige when I ask for this (e.g. brainstorming)
solumunus 11mo ago
You just need to be more explicit. Including “ask clarifying questions” in your prompt makes a huge difference. Not sure if you use Claude Code but if you do, use plan mode for almost every task.
vidarh 11mo ago
A lot of this, I suspect, on the basis of having worked on a supervised fine-tuning project for one of the largest companies in this space, is that providers have invested a lot of money in fine-tuning datasets that sound this way. On the project I did work on, reviewers were not allowed to e.g. answer that they didn't know - they had to provide an answer to every prompt provided. And so when auditing responses, a lot of difficult questions had "confidently wrong" answers because the reviewer tried and failed, or all kinds of evasive workarounds because they knew they couldn't answer. Presumbly these providers will eventually understand (hopefully already has - this was a year ago) that they also need to train the models to understand when the correct answer is "I don't know", or "I'm not sure. I think maybe X, but ..."
- ActorNightly 11mo ago
  Its not the training/tuning, its pretty much the nature of llms. The whole idea is to give a best quess of the token. The more complex dynamics behind the meaning of the words and how those words relate to real world concepts isn't learned.
  - vidarh 11mo ago
    You're not making any sense. The best guess will often be refusals if they see enough of that in the training data, so of course it is down to training And I literally saw the effect of this first hand, in seeing how the project I worked on was actively part of training this behaviour into a major model. As for your assertion they don't learn the more complex dynamics, that was trite and not true already several years ago.
zby 11mo ago
When I expect it to do that I just end my prompt with '. Discuss' - usually this works really well. Not exactly human like - it tries to list all questions and variants at once - but most with good default answers so I only need to engage with a couple of them.
luijk 11mo ago
By default they don't ask questions. You can craft that behaviour with the system message or account settings. Though they will tend to ask 20 questions at once so you have to request it to limit to one question at a time to get a more natural experience.
quietbritishjim 11mo ago
That just means that you need to learn to adapt to the situation: Make your prompt a carefully crafted multi-paragraph description of every detail of the problem and what you want from the solution, with bullet points if appropriate. Maybe it feels a bit sad that you have follow what the LLM wants, but that's just how any tool works really.
max51 11mo ago
>LLMs don't do this They did at the beginning. It used to be that if you wanted a full answer with an intro, bullet points, lists of pros/cons, etc., you had to explicitly ask for it in the prompt. The answers were also a lot more influenced by the tone of the prompt instead of being forced into answering with a specific format like it does right now.
chemotaxis 11mo ago
This is not necessarily a fundamental limitation. It's a consequence of a fine-tuning process where human raters decide how "good" an answer is. They're not rating the flow of the conversation, but looking at how complete / comprehensive the answer to a one-shot question looks like. This selects for walls of overconfident text. Another thing the vendors are selecting for is safety / PR risk. If an LLM answers to a hobby chemistry question in a matter-of-factly way, that's a disastrous PR headline in the making. If they open with several paragraphs of disclaimers or just refuse to answer, that's a win.
morksinaanab 11mo ago
I suspect that's because, trained on website content, seo values more text (see recipe websites). So the default response is fluff.
adamisom 11mo ago
I like Manus's suggested follow-up questions. In fact, sometimes I screenshot them and use Mac's new built-in OCR to copy them, because Manus gives me three options but they disappear if I click one, and sometimes I really like 2 or even all 3.

rafamct 11mo ago

Yes you're totally right! I misunderstood what you meant, let me write six more paragraphs based on a similar misunderstanding rather than just trying to get clarification from you

wlesieutre 11mo ago
My favorite is when it bounces back and forth between the same two wrong answers, each time admitting that the most recent answer is wrong and going back to the previous wrong answer. Doesn't matter if you tell it "that's not correct and neither is ____ so don't try that instead," it likes those two answers and it's going to keep using them.
- deleted 11mo ago
  [deleted]
- BubbleRings 11mo ago
  Ha! Just experienced this. It was very frustrating.
  - amelius 11mo ago
    They really need to add a "punish the LLM" button.
    - danuker 11mo ago
      Some services have the down thumb
      - amelius 11mo ago
        I need something stronger than that.
- heavyset_go 11mo ago
  The false info baked into its context at that point in the conversation and it will get stuck in a local minima trying to generate a response to the given context.
j16sdiz 11mo ago
Once the context is polluted with wrong information, it is almost impossible to get it right again. The only reliable way to recover is to edit your previous question to include the clarification, and let it regenerate the answer.

TimPC 11mo ago

The benchmarks are dumb but highly followed so everyone optimizes for the wrong thing.

morkalork 11mo ago

This drives me nuts when trying to bounce an architecture or coding solution idea off an LLM. A human would answer with something like "what if you split up the responsibility and had X service or Y whatever". No matter how many times you tell the LLM not to return code, it returns code. Like it can't think or reason about something without writing it out first.

basscomm 11mo ago
> Like it can't think or reason about something without writing it out first. LLM's neither think nor reason at all.
- ModernMech 11mo ago
  Right, so LLM companies should stop advertising their models can think and reason.
  - dev1ycan 11mo ago
    But that would burst their valuation bubble as investors would realize it's a technology that already hit its realistic ceiling in usability.
shagie 11mo ago
> Like it can't think or reason about something without writing it out first. Setting aside the philosophical questions around "think" and "reason"... it can't. In my mind, as I write this, I think through various possibilities and ideas that never reach the keyboard, but yet stay within my awareness. For an LLM, that awareness and thinking through can only be done via its context window. It has to produce text that maintains what it thought about in order for that past to be something that it has moving forward. There are aspects to a prompt that can (in some interfaces) hide this internal thought process. For example, the ChatGPT has the "internal thinking" which can be shown - https://chatgpt.com/share/69278cef-8fc0-8011-8498-18ec077ede95 https://chatgpt.com/share/69278cef-8fc0-8011-8498-18ec077ede... - if you expand the first "thought for 32 seconds" bit it starts out with: I'm thinking the physics of gravity assists should be stable enough for me to skip browsing since it's not time-sensitive. However, the instructions say I must browse when in doubt. I’m not sure if I’m in doubt here, but since I can still provide an answer without needing updates, I’ll skip it. (aside: that still makes me chuckle - in a question about gravity assists around Jupiter, it notes that its not time-sensitive... and the passage "I’m not sure if I’m in doubt here" is amusing) However, this is in the ChatGPT interface. If I'm using an interface that doesn't allow internal self-prompts / thoughts to be collapsed then such an interface would often be displaying code as part of its working through the problem. You'll also note a bit of the system prompt leaking in there - "the instructions say I must browse when in doubt". For an interface where code is the expected product, then there could be system prompts that also get in there that try to always produce code.

not_ai 11mo ago

I didn't have the words to articulate some of my frustrations, but I think you summed it up nicely. For example, there's been many times when they take it too literally instead of looking at the totality of the context and what was written. I'm not an LLM, so I don't have perfect grasp on every vocab term for every domain and it feels especially pandering when they repeat back the wrong word but put it in quotes or bold instead of simply asking if I meant something else.

Archelaos 11mo ago

I never expected LLMs to be like an actual conversation between humans. The model is in some respects more capable and in some respects more limited than a human. I mean, one could strive for an exact replica of a human -- but for what purpose? The whole thing is a huge association machine. It is a surealistic inspiration generator for me. This is how it works at the moment, until the next break through ...

keiferski 11mo ago
The disconnect is that companies are trying desperately to frame LLMs as actual entities and not just an inert tech tool. AGI as a concept is the biggest example of this, and the constant push to "achieve AGI" is what's driving a lot of stock prices and investment. A strictly machinelike tool doesn't begin answers by saying "Great question!"
wongarsu 11mo ago
> but for what purpose? I recently introduced a non-technical person to Claude Code, and this non-human behavior was a big sticking point. They tried to talk to Claude similar as to a human, presenting it one piece of information at a time. With humans this is generally beneficial, and they will either nod for you to continue or ask clarifying questions. With Claude this does not work well, you have to infodump as much as possible in each message So even from a perspective of "how do we make this automaton into the best tool", a more human-like conversation flow might be beneficial. And that doesn't seem beyond the technological capabilities at all, it's just not what we encourage in today's RLHF
- jay_kyburz 11mo ago
  I hate when I accidentally hit return halfway through writing my prompt and it gives me two pages of advice about some nonsense half sentence.
- monerozcash 11mo ago
  I haven't tried claude, but Codex manages this fine as long as you prompt it correctly to get started. A lazy example: "This goal of this project is to do x. Let's prepare a .md file where we spec out the task. Ask me a bunch of questions, one at a time, to help define the task" Or you could just ask it to be more conversational, instead of just asking questions. It will do that.

rossant 11mo ago

Lately, ChatGPT 5.1 has been less guilty of this and sometimes holds off answering fully and just asks me to clarify what I meant.

HPsquared 11mo ago

There are plenty of LLM services that have a conversational style. The paragraph blocks thing is just a style.

motoboi 11mo ago

Reflect a moment over the fact that LLMs currently are just text generators. Also that the conversational behavior we see it’s just examples of conversations that we have the model to mimic so when we say “System: you are a helpful assistant. User: let’s talk. Assistant:” it will complete the text in a way that mimics a conversation?. Yeah, we improved over that using reinforcement learning to steer the text generation into paths that lead to problem solving and more “agentic” traces (“I need to open this file the user talked about to read it and then I should run bash grep over it to find the function the user cited”), but that’s just a clever way we found to let the model itself discover which text generation paths we like the most (or are more useful to us). So to comment on your discomfort, we (humans) trained the model to spill out answers (there are thousand of human being right now writing nicely though and formatted answers to common questions so that we can train the models on that). If we try to train the models to mimic long dances into shared meaning we will probably decrease their utility. And we won’t be able anyway to do that because then we would have to have customized text traces for each individual instead of question-answers pairs. Downvoters: I simplified things a lot here, in name of understanding, so bear with me.

MangoToupe 11mo ago
> Reflect a moment over the fact that LLMs currently are just text generators. You could say the same thing about humans.
- smikhanov 11mo ago
  You could, but you’d be missing a big part of the picture. Humans are also (at least) symbol manipulators.
  - MangoToupe 11mo ago
    Same thing
- y0eswddl 11mo ago
  No, you actually can't. Humans existed for 10s to 100s of thousands of years without text. or even words for that matter.

jodrellblank 11mo ago

> LMs don't do this. Instead, every question is immediately responded with extreme confidence with a paragraph or more of text. Having just read a load of Quora answers like this, which did not cover the thing I was looking for, that is how humans on the internet behave and how people have to write books, blog posts, articles, documentation. Without the "dance" to choose a path through a topic on the fly, the author has to take the burden of providing all relevant context, choosing a path, explaining why, and guessing at any objections and questions and including those as well. It's why "this could have been an email" is a bad shout. The summary could have been an email, but the bit which decided on that being the summary would be pages of guessing all the things which what might have been in the call and which ones to include or exclude.

goalieca 11mo ago
This is a recent phenomenon. It seems most of the pages today are SEO optimized LLM garbage with the aim of having you scroll past three pages of ads. THe internet really used to be efficient and i could always find exactly what i wanted with an imprecise google search ~ 15 years ago.
- AznHisoka 11mo ago
  Don’t you get this today with AI Overviews summarizing everything on top of most Google results?
  - Pxtl 11mo ago
    From a UX perspective, the AI overview summary being a multi-paragraph summary makes sense since that was a single query that isn't expected to have conversational context. Where it does not make sense is in conversation-based interfaces. Like, the most popular product is literally called "chat". "I ask a short and vague question and you response with a scrollbar-full of information based on some invalid assumptions" is not, by any reasonable definition, a "chat".
  - i80and 11mo ago
    The AI Overviews are... extremely bad. For most of my queries, Google's AI Overview misrepresents its own citations, or almost as bad, confidently asserts a falsehood or half-truth based on results that don't actually contain an answer to my search query. I had the same issue with Kagi, where I'd follow the citation and it would say the opposite of the summary. A human can make sense of search results with a little time and effort, but current AI models don't seem to be able to.

Traubenfuchs 11mo ago

> When I ask a person something, I expect them to give me a short reply which includes another question/asks for details/clarification. A conversation is thus an ongoing "dance" where the questioner and answerer gradually arrive to the same shared meaning. You obviously never wasted countless hours trying to talk to other people on online dating apps.

herf 11mo ago

Training data is quite literally weighted this way - long responses on Reddit have lots of tokens, and brief responses don't get counted nearly as much. The same goes for "rules" - you train an LLM with trillions of tokens and try to regulate its behavior with thousands. If you think of a person in high school, grading and feedback is a much higher percentage of the training.

9rx 11mo ago
Not to mention that Reddit users seek "confident idiots". Look at where the thoughtful questions that you'd expect to hear in a human social setting end up (hint: Downvoted until they disappear). Users on Reddit don't want to have to answer questions. They want to read the long responses that they can then nitpick. LLMs have no doubt picked up on that in the way they are trained.

jacquesm 11mo ago

If you're paying per token then there is a big business incentive for the counterparty to burn tokens as much as possible.

lxgr 11mo ago
As long as there's no moat (and arguably current LLM inference APIs are far from having one), it arguably doesn't really matter what users pay by. The only thing I care about are whether the answer helps me out and how much I paid for it, whether it took the model a million tokens or one to get to it.
dboon 11mo ago
Making a few pennies more from inference is not even on the radar of the labs making frontier models. The financial stakes are so much higher than that for them.
lkbm 11mo ago
If I'll pay to get a fixed result, sure. I'd expect a Jevons paradox effect: if LLMs got me results twice as fast for the same cost, I'm going to use it more and end up paying more in total. Maximizing the utility of your product for users is usually the winning strategy.

Workaccount2 11mo ago

They are purposely trained to be this way. In a way it's benchmaxxing because people like subservient beings that help them and praise them. People want a friend, but they don't want any of that annoying friction that comes with having to deal with another person.

zenoprax 11mo ago

ChatGPT offered a "robotic" personality which really improved my experience. My frustrations were basically decimated right away and I quickly switched to a more "You get out of it what you put in" mindset. And less than two weeks in they removed it and replaced it with some sort of "plain and clear" personality which is human-like. And my frustrations ramped up again. That brief experiment taught me two things: 1. I need to ensure that any robots/LLMs/mech-turks in my life act at least as cold and rational as Data from Star Trek. 2. I should be running my own LLM locally to not be at the whims of $MEGACORP.

QuercusMax 11mo ago
Sort of a personal modified Butlerian Jihad? Robots / chatbots are fine as long as you KNOW they're not real humans and they don't pretend to be.
danuker 11mo ago
> I should be running my own LLM I approve of this, but in your place I'd wait for hardware to become cheaper when the bubble blows over. I have a i9-10900, and bought an M.2 SSD and 64GB of RAM in july for it, and get useful results with Qwen3-30B-A3B (some 4-bit quant from unsloth running on llama.cpp). It's much slower than an online service (~5-10 t/s), and lower quality, but it still offers me value for my use cases (many small prototypes and tests). In the mean time, check out LLM service prices on https://artificialanalysis.ai/ https://artificialanalysis.ai/ Open source ones are cheap! Lower on the homepage there's a Cost Efficiency section with a Cost vs Intelligence chart.
- zenoprax 11mo ago
  I have a 9070 XT (16 GB VRAM) and it is fast with deepseek-r1:14B but I didn't know about that Qwen model. Most of the 'better' models will crash for lack of RAM. https://dev.to/composiodev/qwen-3-vs-deep-seek-r1-evaluation-notes-1bi1 https://dev.to/composiodev/qwen-3-vs-deep-seek-r1-evaluation... If it runs, it looks like I can get a bit more quality. Thanks for the suggestion.

jstummbillig 11mo ago

a) I find myself fairly regularly irritated by the flow of human-human conversations. In fact, it's more common than not. Of course, I have years of practice handling that more or less automatically, so it rarely raises to the level of annoyance, but it's definitely work I bring to most conversations. I don't know about you but that's not really a courtesy I extend to the LLM. b) If it is, in fact, just one setting away, then I would say it's operating fairly similarly?

ryandrake 11mo ago

LLMs all behave as if they are semi-competent (yet eager, ambitious, and career-minded) interns or administrative assistants, working for a powerful CEO-founder. All sycophancy, confidence and positive energy. "You're absolutely right!" "Here's the answer you are looking for!" "Let me do that for you immediately!" "Here is everything I know about what you just mentioned." Never admitting a mistake unless you directly point it out, and then all sorry-this and apologize-that and "here's the actual answer!" It's exactly the kind of personality you always see bubbling up into the orbit of a rich and powerful tech CEO. No surprise that these products are all dreamt up by powerful tech CEOs who are used to all of their human interactions being with servile people-pleasers. I bet each and every one of them are subtly or overtly shaped by feedback from executives about how they should respond to conversation.

code_for_monkey 11mo ago
thats the audience! Incompetent CEOS!
- LogicFailsMe 11mo ago
  Nearly every woman I know who is an English as a second language speaker is leaning hard into these things currently to make their prose sound more natural. And that has segued into them being treated almost as a confidant or a friend. As flawed as they are currently, I remain astounded that people think they will never improve and that people don't want a plastic pal who's fun to be with(tm). I find them frustrating personally, but then I ask them deep technical questions on obscure subjects and I get science fiction in return.
  - pessimizer 11mo ago
    > I get science fiction in return. And once this garbage is in your context, it's polluting everything that comes after. If they don't know, I need them to shut up. But they don't know when they don't know. They don't know shit.
    - 11mo ago

heresie-dabord 11mo ago

> The thing that bothers me the most about LLMs is What bothers me the most is the seemingly unshakable tendency of many people to anthropomorphise this class of software tool as though it is in any way capable of being human. What is it going to take? Actual, significant loss of life in a medical (or worse, military) context?

gmueckl 11mo ago
It's the fact that these are competent human language word salad generators that messes with human psychology.
- heresie-dabord 11mo ago
  My calculator produces accurate, verifiable results. My calculator is a great tool but it is not a mathematician. Not by a long shot.
  - thfuran 11mo ago
    And you can tell this pretty easily because it can't have a conversation with you.
ux266478 11mo ago
That qualifier only makes the anthropormorphization more sound. Have you actually thought it through? Give an untrained and unspecialized human the power to cause significant loss of life in a medical context in the same exact capacity, and it's all but guaranteed that's the outcome you'll end up with. I think it's important to be skeptical and push back against a lot of the ridiculous mass-adoption of LLMs, but not if you can't actually make a well-reasoned point. I don't think you realize the damage you do when the people gunning for mass proliferation of LLMs in places they don't belong can only find examples of incoherent critique.
- heresie-dabord 11mo ago
  > an untrained and unspecialized human An untrained and unspecialised human can be trained quickly and reliably for the cost of meals and lodging and will very likely actually try to do the right thing because of personal accountability. Delegating responsibility to badly-designed or outright unfit-for-purpose systems because of incoherent confidence is plainly a bad plan. As for the other nuances of your post, I will assume the best intention.

wincy 11mo ago

Cursor Plan mode works like this. It restricts the LLMs access to your environment and will allow you to iteratively ask and clarify and it’ll piece together a plan that it allows you to review before it takes any action. ChatGPT deep research does this but it’s weird and forced because it asks one series of questions and then goes off to the races, spending a half hour or more building a report. It’s frustrating if you don’t know what to expect and my wife got really mad the first time she wasted a deep research request asking it “can you answer multiple series of questions?” Or some other functionality clarifying question. I’ve found Crusor’s plan mode extremely useful, similar to having a conversation with a junior or offshore team member who is eager to get to work but not TOO eager. These tools are extremely useful we just need to get the guard rails and user experience correct.

catigula 11mo ago

Claude doesn't really have this problem.

dominotw 11mo ago

same experience. i try to learn with it but i can't really tell if what its teaching me is actually correct or merely making up when i challenge it with followup questions.

LogicFailsMe 11mo ago

My favorite description of an LLM so far is of a typical 37-year-old male Reddit user. And in that sense, we have already created the AGI.

DoneWithAllThat 11mo ago

When using an LLM for anything serious (such as at work) I have a standard canned postscript along the lines of “if anything about what I am asking is unclear or ambiguous, or if you need more context to understand what I’m asking, you will ask for clarification rather than try to provide an answer”. This is usually highly effective.

cortesoft 11mo ago

Have you used Claude much? It often responds to things with questions

nowittyusername 11mo ago

Its not a magic technology, they can only represent data they were trained on. Naturally most represented data in their training data is NOT conversational. Consider that such data is very limited and who knows how it was labeled if at all during pretraining. But with that in mind, LLM's definitely can do all the things you describe, but a very robust and well tested system prompt has to be used to coax this behavior out. Also a proper model has to be used, as some models are simply not trained for this type of interaction.

heavyset_go 11mo ago

I don't want to talk to a computer like I would a human

gowld 11mo ago

There are billions of humans. Not every one speaks the same way all the time. The default behavior is trying to be useful for most people. It's easy to skip and skim content you don't care about. It's hard to prod and prod to get to to say something you do care about it if the machine is traint to be very concise. Complaining the AI can't read your mind is exceptionally high praise for the AI, frankly.

bwahah4 11mo ago

In the US anyway, most adults read at a middle school level. It's not "masquerading as a human". The majority of humans are functional illiterates who only understand the world through the elementary principles of their local culture. It's the minority of the human species that take what amounts to little more than arguing semantics that need the reality check. Unless one is involved in work that directly impacts public safety (defined as harm to biology) the demand to apply one concept or another is arbitrary preference. Healthcare, infrastructure, and essential biological support services are all most humans care about. Everything else the majority see as academic wank.

__turbobrew__ 11mo ago

The day when the LLM responds to my question with another question will be quite interesting. Especially at work, when someone asks me a question I need to ask for clarifying information to answer the original question fully.

djeastm 11mo ago
Have you tried adding a system prompt asking for this behavior? They seem to readily oblige when I ask for this (e.g. brainstorming)

solumunus 11mo ago

You just need to be more explicit. Including “ask clarifying questions” in your prompt makes a huge difference. Not sure if you use Claude Code but if you do, use plan mode for almost every task.

vidarh 11mo ago

A lot of this, I suspect, on the basis of having worked on a supervised fine-tuning project for one of the largest companies in this space, is that providers have invested a lot of money in fine-tuning datasets that sound this way. On the project I did work on, reviewers were not allowed to e.g. answer that they didn't know - they had to provide an answer to every prompt provided. And so when auditing responses, a lot of difficult questions had "confidently wrong" answers because the reviewer tried and failed, or all kinds of evasive workarounds because they knew they couldn't answer. Presumbly these providers will eventually understand (hopefully already has - this was a year ago) that they also need to train the models to understand when the correct answer is "I don't know", or "I'm not sure. I think maybe X, but ..."

ActorNightly 11mo ago
Its not the training/tuning, its pretty much the nature of llms. The whole idea is to give a best quess of the token. The more complex dynamics behind the meaning of the words and how those words relate to real world concepts isn't learned.
- vidarh 11mo ago
  You're not making any sense. The best guess will often be refusals if they see enough of that in the training data, so of course it is down to training And I literally saw the effect of this first hand, in seeing how the project I worked on was actively part of training this behaviour into a major model. As for your assertion they don't learn the more complex dynamics, that was trite and not true already several years ago.

zby 11mo ago

When I expect it to do that I just end my prompt with '. Discuss' - usually this works really well. Not exactly human like - it tries to list all questions and variants at once - but most with good default answers so I only need to engage with a couple of them.

luijk 11mo ago

By default they don't ask questions. You can craft that behaviour with the system message or account settings. Though they will tend to ask 20 questions at once so you have to request it to limit to one question at a time to get a more natural experience.

quietbritishjim 11mo ago

That just means that you need to learn to adapt to the situation: Make your prompt a carefully crafted multi-paragraph description of every detail of the problem and what you want from the solution, with bullet points if appropriate. Maybe it feels a bit sad that you have follow what the LLM wants, but that's just how any tool works really.

max51 11mo ago

>LLMs don't do this They did at the beginning. It used to be that if you wanted a full answer with an intro, bullet points, lists of pros/cons, etc., you had to explicitly ask for it in the prompt. The answers were also a lot more influenced by the tone of the prompt instead of being forced into answering with a specific format like it does right now.

chemotaxis 11mo ago

This is not necessarily a fundamental limitation. It's a consequence of a fine-tuning process where human raters decide how "good" an answer is. They're not rating the flow of the conversation, but looking at how complete / comprehensive the answer to a one-shot question looks like. This selects for walls of overconfident text. Another thing the vendors are selecting for is safety / PR risk. If an LLM answers to a hobby chemistry question in a matter-of-factly way, that's a disastrous PR headline in the making. If they open with several paragraphs of disclaimers or just refuse to answer, that's a win.

morksinaanab 11mo ago

I suspect that's because, trained on website content, seo values more text (see recipe websites). So the default response is fluff.

adamisom 11mo ago

I like Manus's suggested follow-up questions. In fact, sometimes I screenshot them and use Mac's new built-in OCR to copy them, because Manus gives me three options but they disappear if I click one, and sometimes I really like 2 or even all 3.