Grammarly is using our identities without permission, https://www.theverge.com/ai-artificial-intelligence/890921/g..., https://archive.ph/1w1oO
This is revolting at so many levels.
https://www.chiffandfipple.com/t/kenny-g-as-necrophile-long-...
You don't bring the dead virtually back to life to perform tricks for you.
I probably did not. Then I would have written that. They are fucking over the dead. They are clearly not communicating with the dead.
> Be kind. Don't be snarky. Converse curiously; don't cross-examine. Edit out swipes.
>> Be kind. Don't be snarky. Converse curiously; don't cross-examine. Edit out swipes.
Which part was snarky, excessively hostile or unprofessional?
You see, the comment that I replied to made an assumption, that assumption is embedded in the word 'probably'. The person that wrote that presumes to know what I intend. I corrected that. Clarified it and moved on. If that seems hostile and snarky to you then I'm happy to be educated. For myself, I think the comment I replied to could have been phrased as a question rather than a statement.
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.
https://www.sciencedirect.com/science/article/pii/S0749596X2...
i can ask it to tell me how to write like a person X right now.
If you think “the tacit knowledge and conscious/subconscious reasoning mix that caused X to write like X” can be meaningfully captured by some 1-page “style guide” like llmtropes, I’m not sure what to tell you. Such a style description would be informed by a soup of reviewers that most certainly cannot write like X even with their stronger and more nuanced observations than what the LLM picked up.
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.
this shows that you have very less idea on how llm's work.
LLM that is trained only on john steinbeck will not work at all. it simply does not have the generalised reasoning ability. it necessarily needs inputs from every source possible including programming and maths.
You have completely ignored that LLMs have _generalised_ reasoning ability that it derives from disparate sources.
This is not the same thing as reasoning.
LLMs are pattern matchers. If you trained an llm only to map some input to the output of John Steinbeck, then by golly that's what it'll be able to do. If you give it some input that isn't suitably like any of the input you gave it during training, then you'll get some unpredictable nonsense as output.
> If you trained an llm only to map some input to the output of John Steinbeck
this is literally not possible because the llm does not get generalised reasoning ability. this is not a useful hypothetical because such an llm will simply not work. why do you think you have never seen a domain specific model ever?
if you wanted to falsify this claim: "llm's cant reason" how would one do that? can you come up with some examples that shows that it can't reason? what if we come up with a new board game with some rules and see if it can beat a human at it. just feed the rules of the game to it and nothing else.
here is gpt-5.4 solving never before seen mathematics problems: https://epochai.substack.com/p/gpt-54-set-a-new-record-on-fr...
you could again say its just pattern matching but then i would argue that its the same thing we are doing.
why do you think that's the case? lets start from here.
the real answer is that you get benefits from having data from many sources that add up expontentially for intelligence.
> LLMs are pattern matchers
but lets try to falsify this. can you come up with a prompt that clearly shows that LLM's can't reason?
Isn't this obvious? There is not enough latent knowledge of math there to enable current LLMs to approximate anything resembling an integral.
Its obvious to you.
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)
LLMs can reason about integrals as well as in a literature context. You suggested that if it’s not trained on literature then it can’t reason about it. But why does that matter?
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?
can you give a specific example of what an llm can't do? be specific so we can test it.
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.
To really recreate his writing style, you would need the notes he started with for himself, the drafts that never even made it to his editor, the drafts that did make to the editor, all the edits made, and the final product, all properly sequenced and encoded as data.
In theory, one could munge this data and train an LLM and it would probably get significantly better at writing terse prose where there are actually coherent, deep things going on in the underlying story (more generally, this is complicated by the fact that many authors intentionally destroy notes so their work can stand on its own--and this gives them another reason to do so). But until that's done, you're going to get LLMs replicating style without the deep cohesion that makes such writing rewarding to read.
But authors have not done this work alone. Grammarly is not going to sell "get advice from the editorial team at Vintage" or "Grammarly requires your wife to type the thing out first, though"
I'll also note that no human would probably want advice from the living versions of the author themselves.
I can do it at the moment with Shakespeare an LLMs.
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.
It's certainly possible to mimic many aspects of a notable writer's published style. ("Bad Hemingway" contests have been a jokey delight for decades.) But on the sliding scale of ingenious-to-obnoxious uses for AI, this Grammarly/Superhuman idea feels uniquely misguided.
Imagine a interviewing a particularly diligent new grad. They've memorized every textbook and best practices book they can find. Will that alone make them a senior+ developer, or do they need a few years learning all the ways reality is more complicated than the curriculum?
LLMs aren't even to that level yet.
And that's often inaccurate - just as much as asking startup founders how they came to be.
Part of it is forgot, part of it is don't know how to describe it and part of it is don't want to tell you so.
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?
i again don't get what the point is?
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 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.
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.
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?
If I trained (or, more likely, fine-tuned) an LLM to generate code like what's found in an individual's GitHub repositories, could you comfortably say it writes code the same way as that individual? Sure, it will capture style and conventions, but what about our limitations? What do you think happens if you fine-tune a model to write code like a frontend developer and ask it to write a simple operating system kernel? It's realistically not in their (individual) data but the response still depends on the individual's thought process.
Look, I don't think you understand how LLM's work. Its not about fine tuning. Its about generalised reasoning. The key word is "generalised" which can only happen if it has been trained on literally everything.
> It's relevant for data it hasn't been trained on
LLM's absolutely can reason on and conceptualise on things it has not been trained on, because of the generalised reasoning ability.
Of course, but reasonable behavior across all humans is not the same as what one specific human would do. An individual, depending on the scenario, might stick to a specific choice because of their personality etc. which is not always explained, and heavily summarized if it is.
this is not true, any examples?
I get that you're into AI products and ok, fine. But no you have not "studied [Shakespeare] greatly" nor are you "able to write like [Shakespeare]." That's the one historical entity that you should not have chosen for this conversation.
This bot is likely just regurgitating bits from the non-fiction writing of authors like an animatronic robot in the Hall of Presidents. Literally nobody would know if the LLM was doing even a passable job of Truman Capote-ing its way through their half-written attempt at NaNoWriMo
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...
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.
can you provide a _single_ example where LLM might fail? lets test this now.
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).
Find an interview where Turpeinen describes her method for writing Beasts of the Sea, e.g.: https://suffolkcommunitylibraries.co.uk/meet-the-author-iida...
Now ask it to produce a short story about a topic unrelated to Beasts of the Sea, let's say a book about the moonlanding.
A human doing this exercise will produce a text with the same feel as Beasts of the Sea, but an LLM-produced text will have nothing in common with it.
why are you bringing this constraint?
If someone has already done the work of giving an example of how to produce text according to a process, we have no way of knowing if the LLM has followed the process or copied the existing example.
And my point of course is that copying examples is the only way that LLMs can produce text. If you use an author who has been so analyzed to death that there are hundreds of examples of how to write like them, say, Hemingway, then that would not prove anything, because the LLM will just copy some existing "exercise in writing like Hemingway".
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.
"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.
They absolutely do not. If you "ask it how it came up with the process in natural language" with some input, it will produce an output that follows, because of the statistics encoded in the model. That output may or may not be helpful, but it is likely to be stylistically plausible. An LLM does not think or understand; it is merely a statistical model (that's what the M stands for!)
i can prove that it does have understanding because it behaves exactly like a human with understanding does. if i ask it to solve an integral and ask it questions about it - it replies exactly as if it has understood.
give me a specific example so that we can stress test this argument.
for example: what if we come up with a new board game with a completely new set of rules and see if it can reason about it and beat humans (or come close)?
LLMs can't consistently win at chess https://www.nicowesterdale.com/blog/why-llms-cant-play-chess
Now, some of the best chess engines in the world are Neural Networks, but general purpose LLMs are consistently bad at chess.
As far as "LLM's don't have understanding", that is axiomatically true by the nature of how they're implemented. A bunch of matrix multiplies resulting in a high-dimensional array of tokens does not think; this has been written about extensively. They are really good for generating language that looks plausible; some of that plausable-looking language happens to be true.
https://maxim-saplin.github.io/llm_chess/
ets not cherry pick and actually see benchmarks please. i would say even ~1000 elo means that it can reason better than the average human.
Most importantly, negative but unused signals might not be available if the text does not mention it.
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 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.
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.
This Grammarly thing seems to be a bastardized form of that not even sparing the dead.
I'd say that there was some incentive by the AI companies to muddle up the water here.
i give the LLM my codebase and it indeed learns about it and can answer questions.
Unless you are actually fine tuning models, in which case sure, learning is taking place.
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.
For me, Grammarly gives me the same impression as Datadog, but I have no explanation for why I feel that way.
If it feels like Grammarly does not respect your right to digital sovereignty, it is because it does not.
Seems pretty likely usage of Grammarly's core product has cratered in the past few years. Not totally hard to imagine one of the big AI labs paying their legal fees in exchange for putting this out there and kick starting the legal process on some of these issues.
So IMO they are just flinging things at the wall trying to find a way back.
It reminds me of winzip.
Seems like there could be others that are better.
https://github.com/theJayTea/WritingTools/blob/main/Windows_...
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.
One lesson they might draw from the negative press is to offer a more open-ended interface, like ChatGPT, where for years people have already been asking "Pretend you are X and review my writing". This interface design pattern gives the press nowhere to point their angry fingers
Does it add any value for writers?
We believed this was coming and that the best way to handle it was give the real person control over their persona to grow/edit/change and train it as they see fit.
I actually own the patent on building an expert persona based on the context of the prompt plus the real persons learned information manifold...
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.
[1] https://plugins.jetbrains.com/plugin/12175-natural-languages... [2] https://languagetool.org -- warning: is coated in somewhat-misleading AI keywords [3] https://github.com/languagetool-org/languagetool
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
Or do they?
Words paint the picture, but the meaning of the picture is what matters.
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
[0]: https://en.wikipedia.org/wiki/Health_of_Vincent_van_Gogh
That train left at full steam when companies scraped the whole internet and claimed it was fair use. Now it's a slippery slope covered with slime.
I believe there'll be no slowing down from now on.
They are doing something amazing, will they ask for permission? /s.
"The work is public, hence the name. It's well known, it's in the data. Who cares".
What will they do next? Create similar publications with domainsquatting and write all-AI articles with the "public" names?
Is it still fair use, then?
Big difference between "AI, rewrite this passage to sound more like Hunter S Thompson" and "Grammarly-brand unauthorized digital agent Hunter S Thompson, provide a critique of my writing"
Let's see what company values informed this decision [0].
> At Grammarly, it all starts with our EAGER values: Ethical, Adaptable, Gritty, Empathetic, and Remarkable. These values are guiding lights that keep the Grammarly experience compassionate and our business competitive.
[0]: https://www.grammarly.com/about
Sounds like something I'd expect to see on a banner in an elementary school classroom.