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> LLMs don't understand. It's mind-boggling to me that large parts of the tech industry think that. I think you might be tied to a definition of "understanding
by motorest 1y ago
> LLMs don't understand. It's mind-boggling to me that large parts of the tech industry think that.
I think you might be tied to a definition of "understanding" that doesn't really apply.
If you prompt a LLM with ambiguous instructions, it requests you to clarify (i.e., extend prompt to provide more context) and once you do the LLM outputs something that exactly meets the goals of the initial prompt, does it count as understanding?
If it walks like a duck and quacks like a duck, it's a duck,or something so close to a duck that we'd be better off calling it that.
- dingnuts 1y ago[flagged]
- Uehreka 1y ago> NO. YES! Sometimes. You’ll often hear the term “zero-shot generation”, meaning creating something new given zero examples, this is something many modern models are capable of.
- childintime 1y agoIn my experience it generally has a very good understanding and does generate the relevant test cases. Then again I don't give it a grammar, I just let it generalize from examples. In my defense I've tried out some very unconventional languages. Grammars are an attempt at describing a language. A broken attempt if you ask me. Humans also don't like them.
- yusina 1y agoFor natural language you are right. The language came first, the grammar was retrofitted to try to find structure. For formal languages, which programming languages (and related ones like query languages, markup languages, etc) are an instance of, the grammar defines the language. It come first, examples second. Historically, computers were very good at formal languages. With LLMs we are entering a new age where machines are becoming terrible at something they once excelled at. Have you lately tried asking Google whether it's 2025? The very first data keeping machines (clocks) were also pretty unreliable at that. Full circle I guess.
- bobxmax 1y ago> If you define a grammar for a new programming language and feed it to an LLM and give it NO EXAMPLES can it write code in your language? Of course it can. It will experiment and learn just like humans do. Hacker news people still think LLMs are just some statistical model guessing things.
- yusina 1y ago> Hacker news people still think LLMs are just some statistical model guessing things. That's exactly what they are. It's the definition of what they are. If you are talking about something that is doing something else, then it's not an LLM.
- abletonlive 1y agoyawn. this is so reductive it's almost not even worth talking about. you can prove yourself wrong within 30 minutes but you choose not to.
- yusina 1y agoSorry, I don't follow. How do you define LLMs?
- bobxmax 1y agoNo, it's not. Arrogant developers on HN like to parrot this, but it isn't true. The power of LLMs are the emergent properties that have emerged which weren't specifically taught to the model. That is not a property that exists in statistical, largely more deterministic models. If you think it's just a giant token prediction machine you've just ignored the last 5 years.
- motorest 1y ago> If you define a grammar for a new programming language and feed it to an LLM and give it NO EXAMPLES can it write code in your language? Neither does your average human. What's your point?
- yusina 1y agoThat's the bar now? Some tech is great if the average human is worse at it?
- Dylan16807 1y agoThere are lots of bars depending on context. But being better than an average human is usually one of the higher bars.
- catlifeonmars 1y agoWell it depends. For example, calculators have been around for a while and a calculator that only performs better than the average human is not very useful. Sorting algorithms are another example. Autocomplete/intellisense in an IDE is probably the most salient example. An autocomplete that performs _as well_ as the average programmer is, well, totally useless.
- Dylan16807 1y agoI'm not sure what you mean. Autocomplete usually just gives a big list of options for the next word, so even getting the most relevant ones to the top would be helpful in less-memorized codebases, and actually filling out entire lines as well as an average programmer is not useless at all.
- catlifeonmars 1y agoOh I agree about filling out entire lines. I use copilot to write boilerplate based on a template pattern I provide all the time. It’s not very precise though. Autocomplete will give me a list of valid APIs for current given version of any library sorted by most recently used with a locality bonus. Copilot on the other hand does not have that level of precision. Two different tools that I use for slightly different things.
- vidarh 1y agoI've had LLMs do that more than once.
- yunwal 1y ago> If you define a grammar for a new programming language and feed it to an LLM and give it NO EXAMPLES can it write code in your language? Yes. If you give models that have a cutoff of 2024 the documentation for a programming language written in 2025 it is able to write code in that language.
- catlifeonmars 1y agoI have found this not to work particularly well in practice. Maybe I’m holding it wrong? Do you have any examples of this?
- acchow 1y ago> If you prompt a LLM with ambiguous instructions, it requests you to clarify (i.e., extend prompt to provide more context) It does not understand that it needs clarification. This behavior is replicated pattern
- AlecSchueler 1y agoWhat is the difference? What would actual understanding look like?
- deleted 1y ago[deleted]
- accCer 1y agoIt depends on which human feedback was used to train the model. For humans, there are various communication models like the four-sides model. If the dataset has annotations for the specific facets of the communication model, then an LLM trained on this dataset will have specific probabilities that replicate that communication model. You may call this understanding what the prompter says, but it's just replication for me.
- bandrami 1y agoThe difference comes when it receives novel input
- kenjackson 1y agoWhat is novel input?
- spartanatreyu 1y agoSomething it hasn't seen in it's training before. Such as, trying to get an AI to create a design mockup of mastodon but it keeps falling over at laying out the page correctly: https://social.coop/@scottjenson/114593227688326501 https://social.coop/@scottjenson/114593227688326501
- baobun 1y agoI think most of us agree with Searle that a Chinese room does not understand Chinese. https://en.m.wikipedia.org/wiki/Chinese_room https://en.m.wikipedia.org/wiki/Chinese_room
- anorwell 1y agoNor does a neuron. Argumentum ad populum, I have the impression that most computer scientists, at least, do not find Searle's argument at all convincing. Too many people for whom GEB was a formative book.
- deleted 1y ago[deleted]
- intended 1y ago> If it walks like a duck and quacks like a duck, it's a duck,or something so close to a duck that we'd be better off calling it that. Saying “LLMs match understanding well enough”, is to make the same core error if we were to say “rote learning is good enough” in a conversation about understanding a subject. The issue is that they can pass the test(s), but they dont understand the work. This is the issue with a purely utilitarian measure of output.