4 ms·

Self-contradictory policy. > Reporters may use AI tools vetted and approved for our workflow to assist with research, including navigating large volumes of mat

Self-contradictory policy. > Reporters may use AI tools vetted and approved for our workflow to assist with research, including navigating large volumes of material, summarizing background documents, and searching datasets. If this is their official policy, Ars Technica bears as much responsibility as the author they fired for the fabricated reporting. LLMs are terrible at accurately summarizing anything. They very randomly latch on to certain keywords and construct a narrative from them, with the result being something that is plausibly correct but in which the details are incorrect, usually subtly so, or important information is omitted because it wasn't part of the random selection of attention. You cannot permit your employees to use LLMs in this manner and then tell them it's entirely their fault when it makes mistakes, because you gave them permission to use something that will make mistakes 100% without fail. My takeaway from this is to never trust anything that Ars reports because their policy is to rely on plausible generated fictional research and their solution to getting caught is to fire employees rather than taking accountability for doing actual research. --- Edit: Two replies have found complaint with the fact that I didn't quote the following sentence, so here you go: > Even then, AI output is never treated as an authoritative source. Everything must be verified. If I wasn't clear, I consider this to be part of what makes the policy self-contradictory. In my eyes, this is equivalent to providing all of your employees with a flamethrower, and then saying they bear all responsibility for the fires they start. "Hey, don't blame us for giving them flamethrowers, it's company policy not to burn everything to the ground!". Rather than firing the flamethrower-wielding employees when the inevitable burning happens, maybe don't give them flamethrowers.

brey 6mo ago
The next sentence after your quoted section: “Even then, AI output is never treated as an authoritative source. Everything must be verified.”
- applfanboysbgon 6mo ago
  Any verification process thorough enough to catch all LLM fabrications would take more work than simply not using the LLM in the first place. If anything verifying what an LLM wrote is substantially more difficult than just reading the material it's "summarising", because you need to fully read and comprehend the material and then also keep in mind what the LLM generated to contrast and at that point what the fuck are you even doing? I believe this policy can never result in a positive outcome. The policy implicitly suggests that verification means taking shortcuts and letting fabrications slip through in the name of "efficiency", with the follow-up sentence existing solely so that Ars won't take accountability for enabling such a policy but instead place the blame entirely on the reporters it told to take shortcuts.
  - Paracompact 6mo ago
    > I believe this policy can never result in a positive outcome. I get where you're coming from (I'm learning more and more over time that every sentence or line of code I "trust" an AI with, will eventually come back to bite me), but this is too absolutist. Really, no positive result, ever, in any context? We need more nuanced understanding of this technology than "always good" or "always bad."
    - applfanboysbgon 6mo ago
      I didn't say in any context. I'm specifically talking about this policy on journalistic research.
    - bandrami 6mo ago
      If you need accuracy, an LLM is not the tool for that use case. LLMs are for when you need plausibility. There are real use cases for that, but journalism is not one of them.
  - JumpCrisscross 6mo ago
    > Any verification process thorough enough to catch all LLM fabrications would take more work than simply not using the LLM in the first place Sometimes you have a weak hunch that may take hours to validate. Putting an LLM to doing the preliminary investigation on that can be fruitful. Particularly if, as if often the case, you don't have a weak hunch, but a small basket of them.
    - Mordisquitos 6mo ago
      You can prompt LLMs to scan thousands of documents to generate text validating your hunches. In some cases those validated hunches may even be correct.
      - Eisenstein 6mo ago
        It's easy to get an LLM to make any argument you like based on whatever data is available. Those arguments are going to be trivially bad if that data is bad.
JumpCrisscross 6mo ago
> the author they fired for the fabricated reporting Didn't one of the magazine's editors share the byline?
- bombcar 6mo ago
  Yes but editors don't take the fall, they take the credit. Everything occurred exactly as predicted.
fooker 6mo ago
> LLMs are terrible at accurately summarizing anything. I think you are perhaps stuck in 2023?
- Mordisquitos 6mo ago
  And yet we are discussing this in the context of a reporter having been fired from Ars Technica for publishing an article which included inaccurate LLM-generated summaries in 2026. How come? https://news.ycombinator.com/item?id=47226608 https://news.ycombinator.com/item?id=47226608
  - fooker 6mo ago
    Maybe you should read the article? :) What failed was extracting verbatim quotes, not summarizing. If you want an LLM to do verbatim anything, it has to be a tool call. So I’m not surprised.
    - Mordisquitos 6mo ago
      [dead]
Rekindle8090 6mo ago
[dead]
knighthack 6mo ago
> LLMs are terrible at accurately summarizing anything. They very randomly latch on to certain keywords and construct a narrative from them, with the result being something that is plausibly correct but in which the details are incorrect, usually subtly so, or important information is omitted because it wasn't part of the random selection of attention. I don't know what you've been doing, but the summaries I get from my LLMs have been rather accurate. And in any event, summaries are just that - summaries. They don't need to be 100% accurate. Demanding that is unreasonable.
- carefree-bob 6mo ago
  Yes, search and summarization is where LLMs shine. I use them all the time for that, and much less for code generation. I would say search > summarization > debugging > code gen/image gen
- suddenlybananas 6mo ago
  >They don't need to be 100% accurate. Demanding that is unreasonable. If an intern was routinely making up stuff in the summaries they provided to their bosses, they'd be let go.
- avereveard 6mo ago
  Depends on topic, often what they consider important isn't what is important and details that are essential get out of view. I'm having good success with youtube video, not as much with technical docs.
- lamasery 6mo ago
  The LLM meeting-summary bot in Teams seems accurate… unless you were in the meeting, and also closely read the summary afterward. It misrepresents what people actually said all the time.
enraged_camel 6mo ago
[dead]
furyofantares 6mo ago
I'm not a journalist and just for random things I'm interested in, I have no problem using an LLM to point me in a direction and then directly engage with the source rather than treat any of the LLM output as authoritative. It's easy to do. This is not a flamethrower.
dspillett 6mo ago
> You cannot permit your employees to use LLMs in this manner and then tell them it's entirely their fault when it makes mistakes, because you gave them permission to use something that will make mistakes 100% without fail. Yes you can. The same way Wikipedia (or, way back when, a paper encyclopedia) can be used for research but you have to verify everything with other sources because it is known there are errors and deficiencies in such sources. Or using outsourced dev resource (meat-based outsourced devs can be as faulty as an LLM, some would argue sometimes more so) without reviewing their code before implemeting it in production. Should they also ban them from talking to people as sources of information, because people can be misinformed or actively lie, rather than instead insisting that information found from such sources be sense-checked before use in an article? Personally I barely touch LLMs at all (at some point this is going to wind up DayJob where they think the tech will make me more efficient…) but if someone is properly using them as a different form of search engine, or to pick out related keywords/phrases that are associated with what they are looking for but they might not have thought of themselves, that would be valid IMO. Using them in these ways is very different from doing a direct copy+paste of the LLM output and calling it a day. There is a difference between using a tool to help with your task and using a tool to be lazy. > it's company policy not to burn everything to the ground! The flamethrower example is silly hyperbole IMO, and a bad example anyway because everywhere where potentially dangerous equipment is actually made available for someone's job you will find policies exactly like this. Military use: “we gave them flamethrowers for X and specifically trained them not to deploy them near civilians, the relevant people have been court-martialled and duly punished for the burnign down of that school”. Civilian use: “the use of flamethrowers to initiate controlled land-clearance burns must be properly signed-off before work commences, and the work should only be signed of to be performed by those who have been through the full operation and safety training programs or without an environmental risk assessment”.
- Edman274 6mo ago
  > Yes you can. The same way Wikipedia (or, way back when, a paper encyclopedia) can be used for research but you have to verify everything with other sources because it is known there are errors and deficiencies in such sources. I think that if Wikipedia had no recommendations on good sources for their own articles and did not ever ban sources, companies would not be so sanguine about letting people use Wikipedia. There's an entire internal process associated with evaluating sources, and the expectation when using Wikipedia is that nothing written in an article is going to be sourced from the Daily Mail or Conservapedia, as an example. Also, I do think that there are companies that do have policies against talking to known liars. Given the Wikipedia bans sources and news agencies ban human sources once they've been shown to be unreliable, I don't think it's insane to then have such companies or agencies say that AI shouldn't be used because it's been shown to be unreliable. Obviously there's a balancing act of utility versus accuracy, and Ars has (probably incorrectly) decided that the utility of AI outweighs its inaccuracies. What is frustrating is that AI cannot have a higher accuracy than the median reporter, given a little more time. AI is trained on all digitizable text, including falsehoods and inaccuracies by laypeople. Humans can look up digitizable text using search engines, too. An AI can't follow up on leads or ask anyone questions. There's no world in which synthesizing available data from digitized sources alone ends up with more accurate data than a human with a search engine and the ability to make a phone call. So allowing LLM use at all is a direct admission that seeking out the "truth" is not an important goal because it could never actually improve accuracy and could only worsen it through hallucinated, probable reporting. It's one thing when companies say that they're committed to truth and then secretly their most important overriding concern is their bottom line - it's quite another thing when a company directly says that the bottom line is their most important concern. Imagine the emperor walking through the parade, nude, saying "So what if I am nude? What are you going to do about it?"
inquirerGeneral 6mo ago
[dead]
empath75 6mo ago
They're also allowed to use wikipedia to use research. It has similar sorts of problems.
ChrisMarshallNY 6mo ago
> permit I suspect that it's more like "ordered." Many companies are now requiring staff to use AI. Of course, if the employee screws up, then The Secretary Will Disavow Any Knowledge of Your Activities.
totetsu 6mo ago
Is this a case of Moral crumple zones? where "responsibility for an action may be misattributed to a human actor who had limited control over the behavior" https://www.researchgate.net/publication/351054898_Moral_Crumple_Zones_Cautionary_Tales_in_Human-Robot_Interaction https://www.researchgate.net/publication/351054898_Moral_Cru...
- justonceokay 6mo ago
  Sidebar I like “moral crumple zone” much more than “moral hazard” just because it conjures up a much clearer picture of the problem it depicts.
- Terr_ 6mo ago
  Related concept: Unaccountability machines [0] where the system (electronic or organizational) mainly exists to make things nobody's fault. There's Discworld bit [1] that often comes to mind for me, where the protagonist is reading a press-release by a corporate communications monopoly > The Grand Trunk’s problems were clearly the result of some mysterious spasm in the universe and had nothing to do with greed, arrogance, and willful stupidity. Oh, the Grand Trunk management had made mistakes—oops, “well-intentioned judgments which, with the benefit of hindsight, might regrettably have been, in some respects, in error”—but these had mostly occurred, it appeared, while correcting “fundamental systemic errors” committed by the previous management. No one was sorry for anything, because no living creature had done anything wrong; bad things had happened by spontaneous generation in some weird, chilly, geometric otherworld, and “were to be regretted.” [0] https://press.uchicago.edu/ucp/books/book/chicago/U/bo252799883.html https://press.uchicago.edu/ucp/books/book/chicago/U/bo252799... [1] Going Postal by Terry Pratchett
jaredwiener 6mo ago
I have a little bit of a bias here, as I am building forth.news, which is an AI-powered news platform -- but I am also a former journalist. It's not necessarily contradictory. I see this more like giving your employees cars, but telling them they are responsible if they get into accidents. All of this is entirely predicated on expectation and responsibility. First, mark something as being AI if it cannot be verified, and verify everything that you can. Forth is using AI so we can detect and push out stories as quickly as possible, getting breaking news out there as soon as it breaks. Our summaries are AI, but marked as AI. Our underlying source information is right there and cited. We try to be as transparent as possible about the tools we are using, and the tradeoffs. Every journalist should instinctively and reflexively double check everything, regardless of the source. There's an old maxim, "if your mom tells you she loves you, check it out." Being from an LLM doesn't change that.
bredren 6mo ago
> ...this is equivalent to providing all of your employees with a flamethrower, and then saying they bear all responsibility for the fires they start. This is essentially the policy of most SWE groups but with PRs merged, though right? You can / should use AI to accelerate SWE workflows and assist in reviews but if you merge something that is bad or breaks production that is on you. > "Hey, don't blame us for giving them flamethrowers, it's company policy not to burn everything to the ground!". Flamethrowers are inherently dangerous to the operator and are ~intended to be used burn things to the ground. I'm no expert on arms but there is probably a simile with a better fit out there.
- applfanboysbgon 6mo ago
  The way LLMs are being forced upon the workforce in tech is just as bad, actually, yes. > Flamethrowers are inherently dangerous to the operator and are ~intended to be used burn things to the ground. I actually think bringing up this point reinforces the analogy rather than undercutting it. LLMs are ~intended to spread disinformation, eg. Deepseek on 1989, Grok going full Mecha-Hitler, ChatGPT selling out prompts to advertisers. One of the biggest impacts LLMs will have on human society is as a propaganda tool with a reach of billions.
jasonlotito 6mo ago
> If I wasn't clear, I consider this to be part of what makes the policy self-contradictory. Yeah, it's not. And this is a bad take. Adults can easily do this. That you consider this to be burning everything to the ground says more about you than anything.
- Barrin92 6mo ago
  >Adults can easily do this. no they can't, as showcased by the countless of people who don't. This isn't how human beings work. If you give someone a tool that facilitates taking mental shortcuts they will abuse it. You put a code generation tool or even just stackoverflow in front of programmers, they will mindlessly copy-paste. You put the slop generator in front of journalists, they'll write slop. You put the tasty snack and the healthy snack at equal distance in front of someone, guess what they'll eat. The vast majority of people will pick laziness and speed over correctness, always have. So we need tools that if anything slow down work and encourage thinking, not the opposite. You want a better programmer you hand them a debugger and not a chatbot because then they'll be forced to think.
ymolodtsov 6mo ago
I've never seen a summarizing mistake from any modern LLM. What are you even talking about? LLMs hallucinate when they don't have enough context. Not when they're just cutting down the message in their context.
kristianp 6mo ago
These sentences are directly contradictory. How does a user of Ars' AI tools use summaries in their research and at the same time not use them? > summarizing background documents > AI tools must not be used to generate, extract, or summarize material that is then attributed to a named source,

brey 6mo ago

The next sentence after your quoted section: “Even then, AI output is never treated as an authoritative source. Everything must be verified.”

applfanboysbgon 6mo ago
Any verification process thorough enough to catch all LLM fabrications would take more work than simply not using the LLM in the first place. If anything verifying what an LLM wrote is substantially more difficult than just reading the material it's "summarising", because you need to fully read and comprehend the material and then also keep in mind what the LLM generated to contrast and at that point what the fuck are you even doing? I believe this policy can never result in a positive outcome. The policy implicitly suggests that verification means taking shortcuts and letting fabrications slip through in the name of "efficiency", with the follow-up sentence existing solely so that Ars won't take accountability for enabling such a policy but instead place the blame entirely on the reporters it told to take shortcuts.
- Paracompact 6mo ago
  > I believe this policy can never result in a positive outcome. I get where you're coming from (I'm learning more and more over time that every sentence or line of code I "trust" an AI with, will eventually come back to bite me), but this is too absolutist. Really, no positive result, ever, in any context? We need more nuanced understanding of this technology than "always good" or "always bad."
  - applfanboysbgon 6mo ago
    I didn't say in any context. I'm specifically talking about this policy on journalistic research.
  - bandrami 6mo ago
    If you need accuracy, an LLM is not the tool for that use case. LLMs are for when you need plausibility. There are real use cases for that, but journalism is not one of them.
- JumpCrisscross 6mo ago
  > Any verification process thorough enough to catch all LLM fabrications would take more work than simply not using the LLM in the first place Sometimes you have a weak hunch that may take hours to validate. Putting an LLM to doing the preliminary investigation on that can be fruitful. Particularly if, as if often the case, you don't have a weak hunch, but a small basket of them.
  - Mordisquitos 6mo ago
    You can prompt LLMs to scan thousands of documents to generate text validating your hunches. In some cases those validated hunches may even be correct.
    - Eisenstein 6mo ago
      It's easy to get an LLM to make any argument you like based on whatever data is available. Those arguments are going to be trivially bad if that data is bad.

JumpCrisscross 6mo ago

> the author they fired for the fabricated reporting Didn't one of the magazine's editors share the byline?

bombcar 6mo ago
Yes but editors don't take the fall, they take the credit. Everything occurred exactly as predicted.

fooker 6mo ago

> LLMs are terrible at accurately summarizing anything. I think you are perhaps stuck in 2023?

Mordisquitos 6mo ago
And yet we are discussing this in the context of a reporter having been fired from Ars Technica for publishing an article which included inaccurate LLM-generated summaries in 2026. How come? https://news.ycombinator.com/item?id=47226608 https://news.ycombinator.com/item?id=47226608
- fooker 6mo ago
  Maybe you should read the article? :) What failed was extracting verbatim quotes, not summarizing. If you want an LLM to do verbatim anything, it has to be a tool call. So I’m not surprised.
  - Mordisquitos 6mo ago
    [dead]

Rekindle8090 6mo ago

[dead]

knighthack 6mo ago

> LLMs are terrible at accurately summarizing anything. They very randomly latch on to certain keywords and construct a narrative from them, with the result being something that is plausibly correct but in which the details are incorrect, usually subtly so, or important information is omitted because it wasn't part of the random selection of attention. I don't know what you've been doing, but the summaries I get from my LLMs have been rather accurate. And in any event, summaries are just that - summaries. They don't need to be 100% accurate. Demanding that is unreasonable.

carefree-bob 6mo ago
Yes, search and summarization is where LLMs shine. I use them all the time for that, and much less for code generation. I would say search > summarization > debugging > code gen/image gen
suddenlybananas 6mo ago
>They don't need to be 100% accurate. Demanding that is unreasonable. If an intern was routinely making up stuff in the summaries they provided to their bosses, they'd be let go.
avereveard 6mo ago
Depends on topic, often what they consider important isn't what is important and details that are essential get out of view. I'm having good success with youtube video, not as much with technical docs.
lamasery 6mo ago
The LLM meeting-summary bot in Teams seems accurate… unless you were in the meeting, and also closely read the summary afterward. It misrepresents what people actually said all the time.

enraged_camel 6mo ago

[dead]

furyofantares 6mo ago

I'm not a journalist and just for random things I'm interested in, I have no problem using an LLM to point me in a direction and then directly engage with the source rather than treat any of the LLM output as authoritative. It's easy to do. This is not a flamethrower.

dspillett 6mo ago

> You cannot permit your employees to use LLMs in this manner and then tell them it's entirely their fault when it makes mistakes, because you gave them permission to use something that will make mistakes 100% without fail. Yes you can. The same way Wikipedia (or, way back when, a paper encyclopedia) can be used for research but you have to verify everything with other sources because it is known there are errors and deficiencies in such sources. Or using outsourced dev resource (meat-based outsourced devs can be as faulty as an LLM, some would argue sometimes more so) without reviewing their code before implemeting it in production. Should they also ban them from talking to people as sources of information, because people can be misinformed or actively lie, rather than instead insisting that information found from such sources be sense-checked before use in an article? Personally I barely touch LLMs at all (at some point this is going to wind up DayJob where they think the tech will make me more efficient…) but if someone is properly using them as a different form of search engine, or to pick out related keywords/phrases that are associated with what they are looking for but they might not have thought of themselves, that would be valid IMO. Using them in these ways is very different from doing a direct copy+paste of the LLM output and calling it a day. There is a difference between using a tool to help with your task and using a tool to be lazy. > it's company policy not to burn everything to the ground! The flamethrower example is silly hyperbole IMO, and a bad example anyway because everywhere where potentially dangerous equipment is actually made available for someone's job you will find policies exactly like this. Military use: “we gave them flamethrowers for X and specifically trained them not to deploy them near civilians, the relevant people have been court-martialled and duly punished for the burnign down of that school”. Civilian use: “the use of flamethrowers to initiate controlled land-clearance burns must be properly signed-off before work commences, and the work should only be signed of to be performed by those who have been through the full operation and safety training programs or without an environmental risk assessment”.

Edman274 6mo ago
> Yes you can. The same way Wikipedia (or, way back when, a paper encyclopedia) can be used for research but you have to verify everything with other sources because it is known there are errors and deficiencies in such sources. I think that if Wikipedia had no recommendations on good sources for their own articles and did not ever ban sources, companies would not be so sanguine about letting people use Wikipedia. There's an entire internal process associated with evaluating sources, and the expectation when using Wikipedia is that nothing written in an article is going to be sourced from the Daily Mail or Conservapedia, as an example. Also, I do think that there are companies that do have policies against talking to known liars. Given the Wikipedia bans sources and news agencies ban human sources once they've been shown to be unreliable, I don't think it's insane to then have such companies or agencies say that AI shouldn't be used because it's been shown to be unreliable. Obviously there's a balancing act of utility versus accuracy, and Ars has (probably incorrectly) decided that the utility of AI outweighs its inaccuracies. What is frustrating is that AI cannot have a higher accuracy than the median reporter, given a little more time. AI is trained on all digitizable text, including falsehoods and inaccuracies by laypeople. Humans can look up digitizable text using search engines, too. An AI can't follow up on leads or ask anyone questions. There's no world in which synthesizing available data from digitized sources alone ends up with more accurate data than a human with a search engine and the ability to make a phone call. So allowing LLM use at all is a direct admission that seeking out the "truth" is not an important goal because it could never actually improve accuracy and could only worsen it through hallucinated, probable reporting. It's one thing when companies say that they're committed to truth and then secretly their most important overriding concern is their bottom line - it's quite another thing when a company directly says that the bottom line is their most important concern. Imagine the emperor walking through the parade, nude, saying "So what if I am nude? What are you going to do about it?"

inquirerGeneral 6mo ago

[dead]

empath75 6mo ago

They're also allowed to use wikipedia to use research. It has similar sorts of problems.

ChrisMarshallNY 6mo ago

> permit I suspect that it's more like "ordered." Many companies are now requiring staff to use AI. Of course, if the employee screws up, then The Secretary Will Disavow Any Knowledge of Your Activities.

totetsu 6mo ago

Is this a case of Moral crumple zones? where "responsibility for an action may be misattributed to a human actor who had limited control over the behavior" https://www.researchgate.net/publication/351054898_Moral_Crumple_Zones_Cautionary_Tales_in_Human-Robot_Interaction https://www.researchgate.net/publication/351054898_Moral_Cru...

justonceokay 6mo ago
Sidebar I like “moral crumple zone” much more than “moral hazard” just because it conjures up a much clearer picture of the problem it depicts.
Terr_ 6mo ago
Related concept: Unaccountability machines [0] where the system (electronic or organizational) mainly exists to make things nobody's fault. There's Discworld bit [1] that often comes to mind for me, where the protagonist is reading a press-release by a corporate communications monopoly > The Grand Trunk’s problems were clearly the result of some mysterious spasm in the universe and had nothing to do with greed, arrogance, and willful stupidity. Oh, the Grand Trunk management had made mistakes—oops, “well-intentioned judgments which, with the benefit of hindsight, might regrettably have been, in some respects, in error”—but these had mostly occurred, it appeared, while correcting “fundamental systemic errors” committed by the previous management. No one was sorry for anything, because no living creature had done anything wrong; bad things had happened by spontaneous generation in some weird, chilly, geometric otherworld, and “were to be regretted.” [0] https://press.uchicago.edu/ucp/books/book/chicago/U/bo252799883.html https://press.uchicago.edu/ucp/books/book/chicago/U/bo252799... [1] Going Postal by Terry Pratchett

jaredwiener 6mo ago

I have a little bit of a bias here, as I am building forth.news, which is an AI-powered news platform -- but I am also a former journalist. It's not necessarily contradictory. I see this more like giving your employees cars, but telling them they are responsible if they get into accidents. All of this is entirely predicated on expectation and responsibility. First, mark something as being AI if it cannot be verified, and verify everything that you can. Forth is using AI so we can detect and push out stories as quickly as possible, getting breaking news out there as soon as it breaks. Our summaries are AI, but marked as AI. Our underlying source information is right there and cited. We try to be as transparent as possible about the tools we are using, and the tradeoffs. Every journalist should instinctively and reflexively double check everything, regardless of the source. There's an old maxim, "if your mom tells you she loves you, check it out." Being from an LLM doesn't change that.

bredren 6mo ago

> ...this is equivalent to providing all of your employees with a flamethrower, and then saying they bear all responsibility for the fires they start. This is essentially the policy of most SWE groups but with PRs merged, though right? You can / should use AI to accelerate SWE workflows and assist in reviews but if you merge something that is bad or breaks production that is on you. > "Hey, don't blame us for giving them flamethrowers, it's company policy not to burn everything to the ground!". Flamethrowers are inherently dangerous to the operator and are ~intended to be used burn things to the ground. I'm no expert on arms but there is probably a simile with a better fit out there.

applfanboysbgon 6mo ago
The way LLMs are being forced upon the workforce in tech is just as bad, actually, yes. > Flamethrowers are inherently dangerous to the operator and are ~intended to be used burn things to the ground. I actually think bringing up this point reinforces the analogy rather than undercutting it. LLMs are ~intended to spread disinformation, eg. Deepseek on 1989, Grok going full Mecha-Hitler, ChatGPT selling out prompts to advertisers. One of the biggest impacts LLMs will have on human society is as a propaganda tool with a reach of billions.

jasonlotito 6mo ago

> If I wasn't clear, I consider this to be part of what makes the policy self-contradictory. Yeah, it's not. And this is a bad take. Adults can easily do this. That you consider this to be burning everything to the ground says more about you than anything.

Barrin92 6mo ago
>Adults can easily do this. no they can't, as showcased by the countless of people who don't. This isn't how human beings work. If you give someone a tool that facilitates taking mental shortcuts they will abuse it. You put a code generation tool or even just stackoverflow in front of programmers, they will mindlessly copy-paste. You put the slop generator in front of journalists, they'll write slop. You put the tasty snack and the healthy snack at equal distance in front of someone, guess what they'll eat. The vast majority of people will pick laziness and speed over correctness, always have. So we need tools that if anything slow down work and encourage thinking, not the opposite. You want a better programmer you hand them a debugger and not a chatbot because then they'll be forced to think.

ymolodtsov 6mo ago

I've never seen a summarizing mistake from any modern LLM. What are you even talking about? LLMs hallucinate when they don't have enough context. Not when they're just cutting down the message in their context.

kristianp 6mo ago

These sentences are directly contradictory. How does a user of Ars' AI tools use summaries in their research and at the same time not use them? > summarizing background documents > AI tools must not be used to generate, extract, or summarize material that is then attributed to a named source,