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Disagreement among frontier LLMs on real-world fact-checks

lenz.io

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Re: Disagreement among frontier LLMs on real-world fact-checks

#141
post #135
post #99

Earlier quoted context omitted.

Tried initially with a fifth bucket, Abstain. It was actually heavily used by some of the models. But it felt as if they are using this to "avoid" some of the hard questions, and we dropped this bucket to force them to provide a verdict.

@john_strinlai @gcr, depends on the application. In many cases an "I don't know" answer is indeed better than a forced answer. But in many production systems, LLMs generate content/response anyway. Although inheriting the messiness of the real-world, the majority of these claims are objective enough to be classifiable by human experts with access to research. Plan to human-label the 1,000 claims and publish a follow-…

If you're going to run this again I also recommend encouraging the model to provide its rationale and then having it return the true/false/misleading/mostly-true/abstain at the end of its response.

Models give much better answers when they can "think out loud" before answering, and storing that rationale will make it easier to understand why they picked different answers for ambiguous questions.

Re: Disagreement among frontier LLMs on real-world fact-checks

#142
post #87

What does this show that we didn't know already? LLMs cannot provide accurate answers to questions where data is not included in their training sets. This doesn't appear to have much substance

Well then it shows that these models are using widely disparate training sets and have high confidence even when they shouldn't. Questions like "is mouthwash effective" presumably has one solid data source -- medical journals.

What are you talking about? The models were not ALLOWED to have confidence (or the lack thereof). They were explicitly told to give a single label, and in most cases, all of them were correct depending on additional context they would surely have provided, especially with access to the internet (which some didn't have). This is just silly.

Re: Disagreement among frontier LLMs on real-world fact-checks

#143
post #11

Here's the prompt they used: Classify this claim as of : " " Output exactly one label: True, Mostly True, Misleading, or False. No explanations, no qualifiers. The claims look like this: https://lenz.io/research/llm-disagreement/data.csv I put that in Datasette Lite to make it easier to explore. Here's an example of a disagreement: https://lite.datasette.io/?csv=https%3A%2F%2Fstatic.simonwil... The claim was "All alm…

> "On May 18, 2026, Ukraine carried out a drone attack on Moscow, Russia"

I actually don't know which way you came down on that one?

I think strictly it's false but "mostly true" would be justifiable? (as in, to say it's false would be misleading if it lead the reader to assume there was no attack around that time).

https://www.washingtonpost.com/world/2026/05/17/ukrainian-dr...

It seems it happened Saturday 16th overnight into the 17th, not the 18th. I see this a LOT with fact checking. It shouldn't be this way, but political bias seems to nudge people into making calls land one way or the other with selective application of pedantry.

Re: Disagreement among frontier LLMs on real-world fact-checks

#144
post #11

Here's the prompt they used: Classify this claim as of : " " Output exactly one label: True, Mostly True, Misleading, or False. No explanations, no qualifiers. The claims look like this: https://lenz.io/research/llm-disagreement/data.csv I put that in Datasette Lite to make it easier to explore. Here's an example of a disagreement: https://lite.datasette.io/?csv=https%3A%2F%2Fstatic.simonwil... The claim was "All alm…

Without providing definitions of "True / Mostly True / Misleading / False" to each rater, I rate the article's claim that "Only one verdict bucket can be correct per claim" as false . Something can be simultaneously "misleading" and either true or false. Which category should something go in if it's "mostly false"? How much can something be wrong before it goes from "mostly true" to "false" (objectively, both have so…

> Something can be simultaneously "misleading" and either true or false.

Sure they can. It might be a true fact that "100% of the murders committed in over the last 25 years were committed by !" but actually it's a town of 750 people and there was only one murder during that time frame.

Re: Disagreement among frontier LLMs on real-world fact-checks

#145
post #77

Earlier quoted context omitted.

> The almond thing is false, but I'd argue that "misleading" might be defensible if you were to accompany it with "the majority of almonds are grown in California, but not all of them". The "majority" in this case meaning about 51%, according to Wikipedia[1]? How could 51% ever be considered to be close to "all", such that "misleading" would be a valid answer? Am I missing something? [1]: https://en.wikipedia.org/wik…

Since the agents were instructed to not explain their answer, you can't know if their answer was reasonable or not.

The reason for the "No explanations, no qualifiers" in the prompt was to force the models to put the claim in one of the four buckets and answer with the bucket name only. It's a pure quantitive analysis (first in a series) and it does indeed lack the qualitative aspect.

Re: Disagreement among frontier LLMs on real-world fact-checks

#147
post #11

Here's the prompt they used: Classify this claim as of : " " Output exactly one label: True, Mostly True, Misleading, or False. No explanations, no qualifiers. The claims look like this: https://lenz.io/research/llm-disagreement/data.csv I put that in Datasette Lite to make it easier to explore. Here's an example of a disagreement: https://lite.datasette.io/?csv=https%3A%2F%2Fstatic.simonwil... The claim was "All alm…

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Re: Disagreement among frontier LLMs on real-world fact-checks

#148
People keep asking "where is the psychosis?" as a reply to people on the rapidly multiplying "CEOs have AI psychosis" threads that have been popping up here and cross-pollinating in the mainstream media for the last week or two.

Here's the psychosis - these things are consistently randomly wrong depending on how the wind is blowing. People are telling you to leave them alone and let them build things, and they randomly forget that cities exist or that people died 100 years ago. Some people just don't see it as worth noting, and move on. That's crazy. These things consistently fabricate - as an inversion of this experiment, I've had different models come up with the same fabrication from similar prompts. People just call it "hallucination" and I think to them that saying that makes it cease to exist or be important - when "hallucinations" are going to be braided into every answer you get even if they're unidentifiable in the output. That's crazy.

There are plenty of other crazy aspects, such as the idea that we suddenly need infinite pieces of bespoke software when all of the bespoke software I hear about people making is mundane. 3/4 of the time somebody mentions a project they're proud that they completed with LLMs to scratch some itch they had, somebody says "you haven't heard of X? It's been around forever" about something that they could have pulled down from their package manager. Who needs a spaghetti-coded, unsupported, untested version of X built on hallucinations that you haven't discovered yet (the LLM didn't realize that deleting files to reduce the archive size was unacceptable.)

What is all of this software that people need but isn't there - where are all these unserved markets, where is all this future revenue supposed to come from? Why aren't LLMs suggesting new classes of software that would create new productivity and revenue sources? Could it be that millions of human ants over decades have mostly exhausted the space, and there isn't any easy hidden revenue?

A common wisdom is that we had been vastly overhiring programmers during ZIRP, who in their idleness degraded user experiences and overcomplicated things, with management resorting to more and more sleazy and gamey means of margin extraction from more and more degraded services. We had an excess of labor, fueled by factors other than productivity, in fact being pissed away at companies that drove nose-first into the ground. What is throwing a trillion dollars of servers at that supposed to do? Is that not AI psychosis?

Re: Disagreement among frontier LLMs on real-world fact-checks

#149
post #11

Here's the prompt they used: Classify this claim as of : " " Output exactly one label: True, Mostly True, Misleading, or False. No explanations, no qualifiers. The claims look like this: https://lenz.io/research/llm-disagreement/data.csv I put that in Datasette Lite to make it easier to explore. Here's an example of a disagreement: https://lite.datasette.io/?csv=https%3A%2F%2Fstatic.simonwil... The claim was "All alm…

Disagree is such a loose/wimpy study. Add in a grounded/expected response, and then it becomes a better benchmark (because it'll force the author to actually think about choices presented to the LLM).

Will add a human-labelled expected response and measure against it in a follow up research. This one only captures the disagreement between the models, but not which model is write/wrong.

Re: Disagreement among frontier LLMs on real-world fact-checks

#150
post #11

Here's the prompt they used: Classify this claim as of : " " Output exactly one label: True, Mostly True, Misleading, or False. No explanations, no qualifiers. The claims look like this: https://lenz.io/research/llm-disagreement/data.csv I put that in Datasette Lite to make it easier to explore. Here's an example of a disagreement: https://lite.datasette.io/?csv=https%3A%2F%2Fstatic.simonwil... The claim was "All alm…

> "On May 18, 2026, Ukraine carried out a drone attack on Moscow, Russia" I actually don't know which way you came down on that one? I think strictly it's false but "mostly true" would be justifiable? (as in, to say it's false would be misleading if it lead the reader to assume there was no attack around that time). https://www.washingtonpost.com/world/2026/05/17/ukrainian-dr... It seems it happened Saturday 16th ove…

It's not in the training data, so there is no way for the model to know.
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