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From TFA: "Eric Schwitzgebel writes that . . . There’s a huge cognitive difference between nodding along while reading something and actually productively gene

From TFA: "Eric Schwitzgebel writes that . . . There’s a huge cognitive difference between nodding along while reading something and actually productively generating a text. Two reasons: First, once the text is on the page, it’s easy to passively let the approximate word suffice, rather than thinking about word choice in the same effortful, active way we do when generating prose de novo. Second, as I suggested above, I doubt that human beings, even experts, have a good sense of all the factors that shape word choice -- everything they’re being sensitive to. You would have phrased it slightly differently, and even if you don’t know that, or why, a different signal is sent and received." This is the best articulation I've seen of why simply reviewing and copy-editing does not provide remotely the same value as writing from scratch. I spent a considerable amount of time over the past two weeks reviewing and improving a work document that was the output of an LLM. Given the number of people involved and the final level of effort, I'm firmly convinced that writing it manually would have been faster and resulted in a higher quality product. Getting the wording right matters.

onraglanroad 9d ago
While I mostly agree with you, what is the real difference between an editor saying, 'when generating prose de novo' sounds pretentious to an American audience so let's use 'when writing from scratch' instead, versus an LLM giving the same advice? Not that it's necessarily better but it's the kind of thing an editor might pick up and so why would you reject the advice if it's a machine giving it rather than a human?
- layer8 9d ago
  TFA is about having AI do the writing, not about having AI suggest editorial improvements. The parent comment argues against having the AI write the text and having the human merely review it, not against having the AI review human-written text (and the human deciding which of the AI’s suggestions they might apply). See also this existing comment: https://news.ycombinator.com/item?id=49768564 https://news.ycombinator.com/item?id=49768564, which I completely agree with and which is complementary to the above root comment.
  - onraglanroad 9d ago
    True, but I was responding to the comment rather than the article.
    - layer8 9d ago
      I expanded my comment since.
      - onraglanroad 9d ago
        Sure , but that seems like you've agreeing that an AI suggestion is as good as an editor suggestion. What do you see as the difference?
        layer8 9d ago
        I don’t see any conflict with what the root comment wrote. There are probably some differences between human and AI editorial suggestions that could be discussed, but that wasn’t the topic here.
        onraglanroad 9d ago
        It was the topic I was discussing with the person I replied to. If you want to discuss something else just let me know.
lokar 9d ago
Writing is thinking. In many situations where we are called on to write, what is actually needed is thought.
- conmod278 8d ago
  What if I am using Claude as a rubber duck where Claude is the crowdsourced version of all the human thoughts on that topic. It is rerouting all the thoughts ever recorded on that topic through the chatbox to me. Something synergistic could emerge.
  - lokar 8d ago
    I view it as two different things. You need sources of input to stimulate your own thoughts. Historically, talking to others, reading other works, etc. I can see a chatbot as part of that tradition. But in your own mind, your thoughts are still incomplete. The act of writing them down (or, I imagine, in an oral tradition somehow committing to a specific narration) is very important. IMO, when you delegate that to an LLM, you are not really thinking. It's the same as if you explained your ideas to someone (eg in an interview), and then they ghost wrote the work for you.
- itsalwaysgood 8d ago
  Careful consideration is needed. Word selection, grammar, all while keeping the reader in mind. You're sending a message, afterall, so the message should be readable. And then if you want to tell a message with a different tone, again, you must consider differently. The considering can be strengthened with exercise. I can't imagine worrying about signaling as I write: what a huge distraction. The problem is: this type of thinking and writing takes a lot more time and attention. How much consideration should you put into a message? It's a personal answer, but also one that can be constrained by time.
  - lokar 8d ago
    That’s all true, how you convey your ideas matters, and takes effort. I was talking about the step before that: what are your ideas?
gyulai 9d ago
A workflow I've recently discovered for myself that provides a kind of middle ground: I'll ask the LLM to write a first draft in a language that isn't the one the piece should ultimately be in. Then I'll use the LLM's first draft as a blueprint, to write a first draft myself in the intended target language. This fights my brain's temptation to just shut off, and forces every word choice to actually be mine. Then I'll hand that over to the LLM for editorial suggestions and iterate from there.
- telesilla 9d ago
  Excellent idea, I'll try this. And for those who don't speak a second language other than English, you could use leet.
- dkga 9d ago
  Wow, love this! Could even match with a language I am actively learning so it helps that as well!
- BatFastard 9d ago
  I use an antagonistic agent to review and refute its findings, works great.
kelnos 9d ago
I really did like the author's framing there, but I think there is a different, simpler way to put it: Why would you think that asking someone else (that is, another human) to write something (and then reviewing it) is the same thing as writing it yourself? You may trust the other writer's opinions and knowledge, but it will not have the same tone, structure, word choice, understanding, or narrative flow as it would if you were to write it yourself. And when it's an LLM, you should not trust it's "opinions" and "knowledge", because it does not have either of those things. The appearance of those things is just that, an appearance.
- card_zero 9d ago
  There's knowledge in books, and it's ingested all the books, so it does hold knowledge. But I guess that's not how you mean it.
  - jvanderbot 9d ago
    It's funny. I always thought that writing was meant to inform, persuade, or entertain about the subject at hand. But in professional settings, a lot more of the informativeness is about the author, and a lot more of the persuasiveness is I'm worth your time and money. So, if the author is an LLM, and obviously so, what exactly are you informing your audience of (about yourself), and what are you persuading them to do (with your article). I think we now know.
  - sethhochberg 9d ago
    The key difference is that while an encyclopedia holds facts themselves, LLMs trained on that source material encode something more like a highly probable facsimile of those facts - the original fact was lost, LLMs are lossy, but can often be generated again with a decent level of accuracy by churning through stats about words, concepts, and relationships between them. The whole catch is that they can often be regenerated. But LLMs (on their own, in their parametric memory - which is the result of training) don't have any conception of whether what they've generated is a real reproduction of some training material or whether they've invented something false that seemed probable based on their encoded stats. When the probability produces something contrary to what was in the training material, you get hallucinations. They're very, very good predictive text models and can be very, very powerful when hooked up to other tools or outside databases. But its fundamentally lossy technology and all the books having been fed in doesn't guarantee all of the knowledge from those books can be spat back out.
OptionOfT 9d ago
Same flow as translating from your native tongue to a foreign language. Going from foreign to native is easier than native to foreign. The latter requires completely different brain paths and a lot more understanding of the language to actually get to something correct.
itsalwaysgood 8d ago
It's probably annoying the way I oversimplify things, but you reminded me of something I read somewhere. Some Jazz musician was asked to define Jazz. He couldn't really, his answer was something like: 'I don't know. But I'll know when I hear it'. To add something to the discussion directly: thinking requires vocabulary. Vocabulary is the currency of thought. You can usually express an idea with a few thoughts, or many. The audience, and amount of details chosen should always be kept in mind. Writing helps you to remember vocabulary and word choice when expressing ideas.
bulbar 8d ago
> First, once the text is on the page, it’s easy to passively let the approximate word suffice, rather than thinking about word choice in the same effortful, active way we do when generating prose de novo. Meanwhile, me, as an English non-native speaker, ended up discussing two sentences I want send to HR for ten minutes while applying to a job. I do believe there's generally a bias to accept something that's already written. The much bigger reason though is why you let somebody else write it to begin with. It might just be that not thinking carefully about every sentence/wording was the exact thing that made you use AI to begin with. > I spent a considerable amount of time over the past two weeks reviewing and improving a work document that was the output of an LLM. I had the same experience with texts where I have a very detailed expectation of the desired result. This is just a general limitation. For code, there's the saying "the precise description of the solution is already the code". Describing X is a simplification of X, oftentimes it's fine guess the gaps. But when it's not, it didn't help to describe X, you have to manifest X itself.

onraglanroad 9d ago

While I mostly agree with you, what is the real difference between an editor saying, 'when generating prose de novo' sounds pretentious to an American audience so let's use 'when writing from scratch' instead, versus an LLM giving the same advice? Not that it's necessarily better but it's the kind of thing an editor might pick up and so why would you reject the advice if it's a machine giving it rather than a human?

layer8 9d ago
TFA is about having AI do the writing, not about having AI suggest editorial improvements. The parent comment argues against having the AI write the text and having the human merely review it, not against having the AI review human-written text (and the human deciding which of the AI’s suggestions they might apply). See also this existing comment: https://news.ycombinator.com/item?id=49768564 https://news.ycombinator.com/item?id=49768564, which I completely agree with and which is complementary to the above root comment.
- onraglanroad 9d ago
  True, but I was responding to the comment rather than the article.
  - layer8 9d ago
    I expanded my comment since.
    - onraglanroad 9d ago
      Sure , but that seems like you've agreeing that an AI suggestion is as good as an editor suggestion. What do you see as the difference?
      - layer8 9d ago
        I don’t see any conflict with what the root comment wrote. There are probably some differences between human and AI editorial suggestions that could be discussed, but that wasn’t the topic here.
        onraglanroad 9d ago
        It was the topic I was discussing with the person I replied to. If you want to discuss something else just let me know.

lokar 9d ago

Writing is thinking. In many situations where we are called on to write, what is actually needed is thought.

conmod278 8d ago
What if I am using Claude as a rubber duck where Claude is the crowdsourced version of all the human thoughts on that topic. It is rerouting all the thoughts ever recorded on that topic through the chatbox to me. Something synergistic could emerge.
- lokar 8d ago
  I view it as two different things. You need sources of input to stimulate your own thoughts. Historically, talking to others, reading other works, etc. I can see a chatbot as part of that tradition. But in your own mind, your thoughts are still incomplete. The act of writing them down (or, I imagine, in an oral tradition somehow committing to a specific narration) is very important. IMO, when you delegate that to an LLM, you are not really thinking. It's the same as if you explained your ideas to someone (eg in an interview), and then they ghost wrote the work for you.
itsalwaysgood 8d ago
Careful consideration is needed. Word selection, grammar, all while keeping the reader in mind. You're sending a message, afterall, so the message should be readable. And then if you want to tell a message with a different tone, again, you must consider differently. The considering can be strengthened with exercise. I can't imagine worrying about signaling as I write: what a huge distraction. The problem is: this type of thinking and writing takes a lot more time and attention. How much consideration should you put into a message? It's a personal answer, but also one that can be constrained by time.
- lokar 8d ago
  That’s all true, how you convey your ideas matters, and takes effort. I was talking about the step before that: what are your ideas?

gyulai 9d ago

A workflow I've recently discovered for myself that provides a kind of middle ground: I'll ask the LLM to write a first draft in a language that isn't the one the piece should ultimately be in. Then I'll use the LLM's first draft as a blueprint, to write a first draft myself in the intended target language. This fights my brain's temptation to just shut off, and forces every word choice to actually be mine. Then I'll hand that over to the LLM for editorial suggestions and iterate from there.

telesilla 9d ago
Excellent idea, I'll try this. And for those who don't speak a second language other than English, you could use leet.
dkga 9d ago
Wow, love this! Could even match with a language I am actively learning so it helps that as well!
BatFastard 9d ago
I use an antagonistic agent to review and refute its findings, works great.

kelnos 9d ago

I really did like the author's framing there, but I think there is a different, simpler way to put it: Why would you think that asking someone else (that is, another human) to write something (and then reviewing it) is the same thing as writing it yourself? You may trust the other writer's opinions and knowledge, but it will not have the same tone, structure, word choice, understanding, or narrative flow as it would if you were to write it yourself. And when it's an LLM, you should not trust it's "opinions" and "knowledge", because it does not have either of those things. The appearance of those things is just that, an appearance.

card_zero 9d ago
There's knowledge in books, and it's ingested all the books, so it does hold knowledge. But I guess that's not how you mean it.
- jvanderbot 9d ago
  It's funny. I always thought that writing was meant to inform, persuade, or entertain about the subject at hand. But in professional settings, a lot more of the informativeness is about the author, and a lot more of the persuasiveness is I'm worth your time and money. So, if the author is an LLM, and obviously so, what exactly are you informing your audience of (about yourself), and what are you persuading them to do (with your article). I think we now know.
- sethhochberg 9d ago
  The key difference is that while an encyclopedia holds facts themselves, LLMs trained on that source material encode something more like a highly probable facsimile of those facts - the original fact was lost, LLMs are lossy, but can often be generated again with a decent level of accuracy by churning through stats about words, concepts, and relationships between them. The whole catch is that they can often be regenerated. But LLMs (on their own, in their parametric memory - which is the result of training) don't have any conception of whether what they've generated is a real reproduction of some training material or whether they've invented something false that seemed probable based on their encoded stats. When the probability produces something contrary to what was in the training material, you get hallucinations. They're very, very good predictive text models and can be very, very powerful when hooked up to other tools or outside databases. But its fundamentally lossy technology and all the books having been fed in doesn't guarantee all of the knowledge from those books can be spat back out.

OptionOfT 9d ago

Same flow as translating from your native tongue to a foreign language. Going from foreign to native is easier than native to foreign. The latter requires completely different brain paths and a lot more understanding of the language to actually get to something correct.

itsalwaysgood 8d ago

It's probably annoying the way I oversimplify things, but you reminded me of something I read somewhere. Some Jazz musician was asked to define Jazz. He couldn't really, his answer was something like: 'I don't know. But I'll know when I hear it'. To add something to the discussion directly: thinking requires vocabulary. Vocabulary is the currency of thought. You can usually express an idea with a few thoughts, or many. The audience, and amount of details chosen should always be kept in mind. Writing helps you to remember vocabulary and word choice when expressing ideas.

bulbar 8d ago

> First, once the text is on the page, it’s easy to passively let the approximate word suffice, rather than thinking about word choice in the same effortful, active way we do when generating prose de novo. Meanwhile, me, as an English non-native speaker, ended up discussing two sentences I want send to HR for ten minutes while applying to a job. I do believe there's generally a bias to accept something that's already written. The much bigger reason though is why you let somebody else write it to begin with. It might just be that not thinking carefully about every sentence/wording was the exact thing that made you use AI to begin with. > I spent a considerable amount of time over the past two weeks reviewing and improving a work document that was the output of an LLM. I had the same experience with texts where I have a very detailed expectation of the desired result. This is just a general limitation. For code, there's the saying "the precise description of the solution is already the code". Describing X is a simplification of X, oftentimes it's fine guess the gaps. But when it's not, it didn't help to describe X, you have to manifest X itself.