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Disagreement among frontier LLMs on real-world fact-checks
241–250 of 377 posts
Re: Disagreement among frontier LLMs on real-world fact-checks
#242Earlier quoted context omitted.
Better options would have been "True", "False", "Unknown" (which opinions would fall under too). That also includes an interesting assessment of how well LLMs can identify missing information. My guess is they would be a very low number of "unknown" and a much higher level of agreement (assuming equal representation). Unless the RLHF techniques have gotten better at getting an LLM to say "I don't know", which I doubt…
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.
Do you understand how problematic this is?
Re: Disagreement among frontier LLMs on real-world fact-checks
#243As an example, 2026 GPT doesn't even agree with its 2025 self. Last year I asked it to make a hardware comparison and it correctly identified the objectively better option. Recently I asked again and this time it got everything completely backwards.
Models are stochastic. Did you look at pass@k? I wouldn’t be surprised if you saw a regression because these models are extremely complex and impact of various decision making downstream is complex.
I ran this multiple times through GPT-4 and every single time it arrived at the same conclusion. The data was readily available and pretty clear. GPT-5 insisted that the objectively inferior option was better until I gave it my own benchmark data and it was like "Oh okay nevermind".
Gemini's answer was very opinionated and factually correct, whereas Claude gave a more nuanced answer, which was also very good.
Re: Disagreement among frontier LLMs on real-world fact-checks
#244Here'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
#245Here'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…
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Re: Disagreement among frontier LLMs on real-world fact-checks
#246What 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
They will happily google it for you and give you the top reddit comment.
This is worse.
Re: Disagreement among frontier LLMs on real-world fact-checks
#247Earlier quoted context omitted.
Models are stochastic. Did you look at pass@k? I wouldn’t be surprised if you saw a regression because these models are extremely complex and impact of various decision making downstream is complex.
I ran this multiple times through GPT-4 and every single time it arrived at the same conclusion. The data was readily available and pretty clear. GPT-5 insisted that the objectively inferior option was better until I gave it my own benchmark data and it was like "Oh okay nevermind". Gemini's answer was very opinionated and factually correct, whereas Claude gave a more nuanced answer, which was also very good.
This sounds perfectly reasonable and consistent with our current understanding of these models
Re: Disagreement among frontier LLMs on real-world fact-checks
#248Here'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…
Another (IMO fatal) error is they don’t attempt to measure within-model variance. The thing you find when you actually wire up a rigorous eval is that with tool calls like web search you are wide open to infra issues, flakes, and all sorts of non-determinism. They really should be breaking out the numbers for the 3 without search (kinda meaningless for recent factual claims after knowledge cutoff) vs search agents. L…
Good idea about publishing intra-model variance data! Will include in the next version.
Even if we put aside the two middle buckets (Mostly True and Misleading), that are somewhat subject to interpretation and hedging: On 21% of the claims still at least two models provide polar-opposite verdicts (one model saying True, and another saying False)
Re: Disagreement among frontier LLMs on real-world fact-checks
#249between the bad methodology, bad selection of 'facts' (some are predictions, some are opinionated, etc.), and ai-written report without disclosure... i dont get why this so high up on the front page. this is, frankly, a worthless assessment. i classify the entire thing as "misleading"
I really wished these comments were the norm and not the exception.
Re: Disagreement among frontier LLMs on real-world fact-checks
#250I don't get why everyone is hellbent on getting LLMs to perform fact checking. This is not the technology for it. Sure it might sorta kinda work in some circumstances. That doesn't make it a good fit. Think of it like buying a refrigerator for storing clothes.
But people use it for that. So what's your point?
It's a marketing failure (or success, depending on how you see it).
AI is pretty useful for a great many things, but to really attract more and more investment the current technique seems to be convincing people that AI is useful for everything.