The most interesting is the realization that if the LLM's input is only the output of a professional (human), then by definition the LLM cannot mimic the process the (human) professional applied to get from whatever input they had to produce the output.
In other words an LLM can spit out a plausible "output of X", however it cannot encode the process that lead X to transform their inputs into their output.
Actually this is the crux and the nuance which makes discussing LLM specifics a pain in the general space.
If you built an LLM exclusively on the writings and letters of John Steinbeck, you could NOT tell the LLM to solve an integral for you amd expect it to be right.
Instead what you will receive is a text that follows a statistically derived most likely (in accordance to the perplexity tuning) response to such a question.
> If you built an LLM exclusively on the writings and letters of John Steinbeck, you could NOT tell the LLM to solve an integral for you amd expect it to be right.
Isn't this obvious? There is not enough latent knowledge of math there to enable current LLMs to approximate anything resembling an integral.
Now what if we ask the LLM to write about social media? Do you think the output would be similar to what you'd get if we had a time machine to bring the actual man back and have him form his own thoughts firsthand?
It isnt obvious to the person I am responding to, and it isnt obvious to majority of individuals I speak with on the matter (which is why AI, personally, is in the bucket of religion amd politics for polite conversation to simply avoid)
"Explain how to solve" and "write like X" are crucially different tasks. One of them is about going through the steps of a process, and the other is about mimicking the result of a process.
Neural networks most certainly go through a process to transform input into output (even to mimic the results of another process) but it's a very different one from human neutral networks. But I think this is the crucial point of the debate, essentially unchanged from Searle's "Chinese Room" argument from decades ago.
The person in that room, looking up a dictionary with Chinese phrases and patterns, certainly follows a process, but it's easy to dismiss the notion that the person understands Chinese. But the question is if you zoom out, is the room itself intelligent because it is following a process, even if it's just a bunch of pattern recognition?
like OP originally said, the LLM doesn't have access to the actual process of the author, only the completed/refined output.
Not sure why you need a concrete example to "test", but just think about the fact that the LLM has no idea how a writer brainstorms, re-iterates on their work, or even comes up with the ideas in the first place.
This isn't true in general, and not even true in many specific cases, because a great deal of writers have described the process of writing in detail and all of that is in their training data. Claude and chatgpt very much know how novels are written, and you can go into claude code and tell it you want to write a novel and it'll walk you through quite a lot of it -- worldbuilding, characters, plotting, timelines, etc.
It's very true that LLMs are not good at "ideas" to begin with, though.
i don't buy this logic. if i have studied an author greatly i will be able to recognise patterns and be able to write like them.
ex: i read a lot of shakespeare, understand patterns, understand where he came from, his biography and i will be able to write like him. why is it different for an LLM?
You will produce output that emulates the patters of Shakespeare's works, but you won't arrive at them by the same process Shakespeare did. You are subject to similar limitations as the llm in this case, just to a lesser degree (you share some 'human experience' with the author, and might be able to reason about their though process from biographies and such)
As another example, I can write a story about hobbits and elves in a LotR world with a style that approximates Tolkien. But it won't be colored by my first-hand WW1 experiences, and won't be written with the intention of creating a world that gives my conlangs cultural context, or the intention of making a bedtime story for my kids. I will never be able to write what Tolkien would have written because I'm not Tolkien, and do not see the world as Tolkien saw it. I don't even like designing languages
that's fair and you have highlighted a good limitation. but we do this all the time - we try to understand the author, learn from them and mimic them and we succeed to good extent.
that's why we have really good fake van gogh's for which a person can't tell the difference.
of course you can't do the same as the original person but you get close enough many times and as humans we do this frequently.
in the context of this post i think it is for sure possible to mimic a dead author and give steps to achieve writing that would sound like them using an LLM - just like a human.
Not everything works like integrals. Some things don't have a standard process that everyone follows the same way.
Editing is one of these things. There can be lots of different processes, informed by lots of different things, and getting similar output is no guarantee of a similar process.
The process is irrelevant if the output is the same, because we never observe the process. I assume you are arguing that the outputs are not guaranteed to be the same unless you reproduce the process.
If we are talking about human artifacts, you never have reproducibility. The same person will behave differently from one moment to the next, one environment to another. But I assume you will call that natural variation. Can you say that models can't approximate the artifacts within that natural variation?
The point is that you dont become Jimi Hendrix or Eric Clapton even if you spend 20 years playing on a cover band. You can play the style, sound like but you wont create their next album.
Not being Jimi Hendrix or Eric Clapton is the context you are missing. LLMs are Cover Bands...
You can understand his biography and analyses about how shakespeare might have written. You can apply this knowledge to modify your writing process.
The LLM does not model text at this meta-level. It can only use those texts as examples, it cannot apply what is written there to it's generation process.
Yes, what I said should be falsifiable. The burden is on you to give me an example, but I can give you an idea.
You need to show me an LLM applying writing techniques do not have examples in its corpus.
You would have to use some relatively unknown author, I can suggest Iida Turpeinen. There will be interviews of her describing her writing technique, but no examples that aren't from Elolliset (Beasts of the sea).
if we have steps for understanding any author's english and creative process (generally not specific to an author) would you agree then it is possible for an llm to do it?
The real sticking point for me is I don't even believe that authors themselves FULLY understand their process. The idea that anybody could achieve such full introspection as to understand and articulate every little thing that influences their output seems astoundingly improbable.
Repeating a process, yes for sure, even (pseudorandom?) variations on a process. Understanding a process is a different question, and I’m not sure how you would measure that.
In school we would have a test with various questions to show you understand the concept of addition, for example. But while my calculator can perfectly add any numbers up to its memory limit, it has no understanding of addition.
> while my calculator can perfectly add any numbers up to its memory limit, it has no understanding of addition.
"my calculator can perfectly add any numbers up to its memory limit" This kind of anthropomorphic language is misleading in these conversations. Your calculator isn't an agent so it should not be expected to be capable of any cognition.
It’s the degree of generalisability. And LLMs do have understanding. You can ask it how it came up with the process in natural language and it can help - something a calculator can’t do.
Only if the LLM knows the inputs connected to particular outputs, pre-digital era or classified material might not be available, neither informal discussions with other experts.
Most importantly, negative but unused signals might not be available if the text does not mention it.
An LLM can always output steps, but it doesn’t mean they are true, they are great at making up bullshit.
When the “how many ‘r’ in ‘strawberry’” question was all the rage, you could definitely get LLMs to explain the steps of counting, too. It was still wrong.
I’m pretty sure you can think of one yourself, I’m not going to play this game. Now it’s 5.4 Thinking, before that it was 5.3, before that 5.2, 5.1, 5, before that it was 4… At every stage there’s someone saying “oh, the previous model doesn’t matter, the current one is where it’s at”. And when it’s shown the current model can’t do something, there’s always some other excuse. It’s a profoundly bad faith argument, the very definition of moving the goal posts.
I do have a number of examples to give you, but I no longer share those online so they aren’t caught and gamed. Now I share them strictly in person.
Replace "LLM" with "student" and read that again. You don't just blindly give students output, you teach them, like what you are supposed to do with an LLM.
Enough with this analgoy. It's flawed on so many levels. First and foremost, stop devaluing humanitiy and hyping up AI companies by parroting their party line. Second, LLMs don't learn. They can hold a very limited amount of context, as you know. And every time you need to start over. So fuck no, "teaching" and LLM is nothing like teaching an actual human.
„Fitting“ is still too nice of a word choice, because it implies that it’s easy to identify the best solution.
I suggest „randomly adjusting parameters while trying to make things better“ as that accurately reflects the „precision“ that goes into stuffing LLMs with more data.
It was called learning already back when the field was called cybernetics and foundational figures like Shannon worked on this kind of stuff. People tried to decipher learning in the nervous system and implement the extracted principles in machines. Such as Hebbian learning, the Perception algorithm etc. This stuff goes back to the 40s/50s/60s, so things must have gone south pretty early then.
That isn't learning, it can read things in its context, and generate materials to assist answering further prompts but that doesn't change the model weights. It is just updating the context.
Unless you are actually fine tuning models, in which case sure, learning is taking place.
i don't know why you think it matters how it works internally. whether it changes its weights or not is not important. does it behave like a person who learns a thing? yes.
if i showed a human a codebase and asked them questions with good answers - yes i would say the human learned it. the analogy breaks at a point because of limited context but learning is a good enough word.
Maybe because I work on a legacy programming language with far less material in the training? For me it makes a difference because it partly needs to "learn" the language itself and have that in the context, along with codebase specific stuff. For something with the model already knowing the language and only needing codebase specific stuff it might feel different.
Grammarly seemed pretty dead on arrival the moment they added AI features. They would have said a lot more relevant and kept the costs down if they were strictly no-ai imo.
The funny thing is, their core "grammar" engine has to work on a language model + some hard heuristics anyway. So they were on a path to utilize this thing for real good, with concrete benefits.
Generative AI is a plague at this point. Everybody is adding to their wares to see what happens. It's almost like ricing a car. All noise, no go.
This feels like a desperate attempt to stay relevant in a post-LLM world. They’re basically wrapping an LLM in a "professional" skin and calling it an expert review. The problem is that once you start letting an AI "expert" dictate tone and logic, you effectively lobotomize the writer’s original intent. We’re reaching a point where AI is just reviewing other AI-generated text, creating a feedback loop of pure mediocrity. Copium for middle management, if you ask me.
Grammarly even from the start was very distracting to me even as a someone using english as a second language to communicate. I have developed my own taste and way of articulating thoughts, but grammarly (and LLMs today) forced me to remove that layer of personality from my texts which I didn't wanted to let go. Sure I sounded less professional, but that was the image I wanted to project anyways.
Unrelated but surprising to me that I've found built-in grammar checking within JetBrains IDEs far more useful at catching grammar mistakes while not forcing me to rewrite entire sentences.
> The problem is that once you start letting an AI "expert" dictate tone and logic, you effectively lobotomize the writer’s original intent
Isn't that what grammarly has always been, since long before the invention of the transformer? They give you a long list of suggestions, and unless you write a corporate press release half of them are best ignored. The skill is in choosing which half to ignore
It's great. Now that fancy writing is cheap and infinite, fields whose entire scholarship value was in obscurantist jargon bending have to actually start to turn on their brains and care about making more sense than an LLM can.
Maybe not. But academia is going to change. Status will still have to be allocated by some mechanism but the classic journals and reviews based system will crumble under the weight of LLMs. Of course this will upset a great many of people who enjoy the current state of things.
I disagree. You write when you have something to say. A service like Grammarly tries to help you convey what you want to say, but better. What you want to say is still up to you.
Words paint the picture, but the meaning of the picture is what matters.
Children and young students, certainly. Adult students: almost 100%. If writing is your job, then by definition, and your problem is more often finding something to say, not writing it.
You’re not counting all the office workers who have to write reports or emails, or all the scammers who write those websites to manipulate SEO or show you ads.
A few things worth flagging:
On GDPR: Using a named individual's identity to generate commercial AI output isn't obviously covered by "legitimate interest." Affected EU-based individuals likely have real grounds to object or request erasure.
On IP/publicity rights: You can't copyright an editing style — but you absolutely can have a right of publicity claim when a company profits from your name and simulated judgment without consent. The Lanham Act's false endorsement provisions could also be in play here.
The kicker: The "sources" cited by the feature were broken, spammy, or pointed to completely unrelated content. So the defense that suggestions are inspired by someone's actual work may not even hold up technically.
Frankly, I am surprised this was not shut down by their legal counsel (assuming they have one and they actually asked). The legal exposure here is significant. This could be defamation, there are publicity rights issues, copyright, and maybe even criminal liability.
It really feels so wrong to spare nobody, not even dead writer/people.
All it's gonna do is something similar to em-dashes where people who use it are now getting called LLM when it was their writing which would've trained LLM (the irony)
If this takes off, hypothetically, we will associate slop with the writing qualities similar to how Ghibli art is so good but it felt so sloppy afterwards and made us less appreciate the Ghibli artstyle seeing just about anyone make it.
The sad part is that most/some of these dead writers/artists were never appreciated by the people of their time and they struggled with so many feelings and writing/art was their way of expressing that. Van Gogh is an example which comes to my mind.[0] Many struggled from depression and other feelings too. To take that and expression of it and turn it into yet another product feels quite depressing for a company to do
yes i hate that. they still have the chutzpah of keeping doing it. and i am sure it's illegal in multiple legislation. because they are not writing articles where you can cite people, they are selling a product.
If it feels like Grammarly does not respect your right to digital sovereignty, it is because it does not.
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