Only had a brief look at the “facts” that were made to check, many are quite political, where two fact checking organisation of opposite political persuasion would probably disagree more often than 67%.
Disagreement among frontier LLMs on real-world fact-checks
71–80 of 377 posts
Re: Disagreement among frontier LLMs on real-world fact-checks
#72Here'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…
I really struggle to believe that this was just a little
oopsie. I flagged the article, it seems more misleading than the average Claude hallucination.
Re: Disagreement among frontier LLMs on real-world fact-checks
#73Earlier quoted context omitted.
False vs misleading doesn't seem like a disagreement?
Yes, they are much closer verdicts. True and Mostly True are also close. Used Krippendorff's α (ordinal) to not penalize much closer disagreements. 21% of the claims have models that are on the polar opposite sides - at least one True, and at least one False.
Here are the claims with at least one True and at least one False:
https://lite.datasette.io/?csv=https%3A%2F%2Fstatic.simonwil...
A few examples:
> Ruskin Bond was born on May 19, 1934, in Kasauli, Himachal Pradesh, India.
> In the Libra clubs' contract with Grupo Globo for broadcast rights through 2029, the audience-revenue distribution equals 30% of the fixed amount the clubs receive.
Re: Disagreement among frontier LLMs on real-world fact-checks
#74"None of these claims is older than February 15, 2026" All of the models they tested were trained on data from before February 15th ... being asked specific questions about things that happened after they were trained.
Two of the models used have retrieval capabilities and can access newer information via search. Valid point for the other 3 models. All of the claims were submitted after February 15, 2026, but many of them were not time-sensitive (e.g. did not cover events than happened recently).
Re: Disagreement among frontier LLMs on real-world fact-checks
#75Here'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…
For those questions, it wouldn’t surprise me at all if five well-educated intelligent humans disagreed on over two out of three of them.
I would answer “don’t know” on many, but that’s not an option.
Re: Disagreement among frontier LLMs on real-world fact-checks
#76Here'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…
[flagged]
Why would I do that?
My comment here was meant to save people time in understanding the study. I was entirely open about what I did, and provided tools to help other people come to their own conclusions.
I don't think I need to spend more time on this than I have.
Re: Disagreement among frontier LLMs on real-world fact-checks
#77Here'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…
> 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?
Re: Disagreement among frontier LLMs on real-world fact-checks
#78Here'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…
Yup, if anything this should be a guide on how not to eval a model. Furthermore, let's say the labels were non ambiguous, why would we care about alignment between the models? The only number I would personally care about is percentage of correct answers so I know which models to pick. I reckon with clear and non ambiguous prompts that we would see huge agreement if not 100% on real world facts. The huge models are s…
This paper covers only the disagreement between models and established only the floor of the error, based on the disagreement, but not which model is better. Planning to follow up with another study to benchmark against human-labelled verdicts still using a corpus that the models have not seen during training.
Re: Disagreement among frontier LLMs on real-world fact-checks
#79Here'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…
If we’re going to use LLMs as oracles I don’t think the prompt is unreasonable. They are being sold as geniuses and people are treating them as such especially given the characterization of AI in science fiction as overly correct. A perfect tool that has ”genius level intelligence” would answer correctly.
Re: Disagreement among frontier LLMs on real-world fact-checks
#80Earlier quoted context omitted.
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…
If you can consistently construct "true but misleading" content, you may be qualified to work at a major newspaper.
> true but misleading
It seems to me that for many newspapers the bar is now significantly lower, at something like "not quite entirely untrue"