> When asked if Superhuman considered notifying the people named in its AI feature, or requesting their permission, Gay said, “The experts in Expert Review appear because their published works are publicly available and widely cited.”
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.
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.
Interestingly, there is some neuroscience research that transformer architecture resembles "cue based retrieval" in the human brain in some important ways.
"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?
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.
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)
Wait -- I'm fairly certain this is obvious to the person you were responding to. It may not be obvious to a lay person (who may not even know LLMs are trained at all). But I think this is obvious to almost all people with even a small understanding of LLMs.
It’s obvious to me. What point are you trying to make? It’s not religion it’s falsifiable easily.
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?
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?
>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.
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.
LLMs have the ability to convince you that they've "reasoned". sometimes, an application will loop the output of an LLM to its input to provide a "chain of reasoning"
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.
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.
“i can ask it to give a text description of a linear logical math process that has been described in text countless times”
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.
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.
This makes the same error, or a related one. That input is not the lawyer's internal expert process, only the intermediate or (near-) final outcome of it.
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.
First, modern LLMs are not "a huge table of phrases". They are neural networks with billions of learned parameters that generate tokens by computing probability distributions over vocabulary given prior context. There is no lookup table of stored sentences.
Second, Eliza-style bots used explicit scripted pattern matching rules. LLMs instead learn statistical representations from large corpora and can generalize to produce novel sequences that were never present in the training data.
Kent Pitman's Lisp Eliza from MIT-AI's ITS History Project (sites.google.com):
Third, while "pattern matching" is sometimes used informally, it’s misleading technically. Transformers perform high-dimensional vector computations and attention over context to model relationships between tokens. That’s very different from rule-based pattern matching.
You can certainly debate whether LLMs "think", but describing them as "Eliza with a big phrase table" is not an accurate description of how they work.
You have the resources available at your fingertips to learn what the truth is, how LLMs actually work. You could start with Wikipedia, or read Steven Wolfram's article, or simply ask an LLM to explain how it works to you. It's quite good at that, while an Eliza bot certainly can't explain to you how it works, or even write code.
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.
In a similar vein, I'll bet you that rather soon Faceborg will announce a service to keep the deceased "alive" on the platform, posting and commenting away. For plenty of accounts there's plenty of training material.
Funnily enough FB already has the patent for this: using LLMs for "simulating the user when the user is absent from the social networking system, for example... if the user is deceased."
They already have a feature where a profile can be marked as a memorial page or some such thing, I don't think this is so far fetched considering how ghoulish that robot Zucc is.
Oh shit, that never even crossed my mind. They don't even need the content creators any more, they can just keep everybody on the platform even though they have left. Faking likes and faking posts for eternity.
What objection? IANAL but offering something "inspired by" is fair use. We have not yet reached a point where you can get a government sanctioned monopoly for your writing style or personality.
Unless they're outright marketing this as "endorsed by" or similar, there is no case.
There is an unimaginably large gulf of distance between an individual thinking about what an author would say about their writing, and a corporate entity selling the "opinion" of an LLM asked to act like someone.
The writer of the article confesses to using LLMs to improve their grammar. Writer is then upset Grammarly correctly attributes the recommendation he is getting to someone they know.
A nothing burger essentially.
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.
I spent a great deal of time trying to do this at allofus.ai with a team of ex-googlers with our goal being to help creators eventually 'own' their personas and drive and compete to use them to help end users.
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...
It almost seems like this whole feature is designed to invite law suits.
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.
LLMs basically made Grammarly irrelevant as a product. Why have a tool to correct your grammar when you can just have it write the whole piece for you. And one things LLMs do well is construct grammatically correct text.
So IMO they are just flinging things at the wall trying to find a way back.
As Annie Duke said in her book Quit, "quitting on time usually feels like quitting too early." Grammarly was a great in the 2010s, but now it's too easily replaced.
It seems like there are many apps that can be run locally that use LLMs. Although I haven't used this, I found it on reddit and it's made by a student. https://github.com/theJayTea/WritingTools
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.
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
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.
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.
JetBrains’s default grammar checking plugin[1] is actually built on languagetool[2], a pretty decent grammar checker that also happens to be partly open source and self-hostable[3]. Sadly, they have lately shoved in a few (thankfully optional) crappy LLM-based features (that don’t even work well in the first place) and coated their landing page in endless AI keywords, but their core engine is still more traditional and open-source, and hasn’t really seemed to change in years. You can just run it on your own device and point their browser and editor extensions to it.
> 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
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.
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.
That's a beautiful Kafkatrap you've constructed. Not much of an argument though. Maybe there's another explanation for this though. Perhaps you think you know much more about different fields than you actually do?
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.
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
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.
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