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Disagreement among frontier LLMs on real-world fact-checks

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

#131

Post by anilgulecha »

simonw wrote: 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).

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

#132

Post by SkyBelow »

torben-friis wrote: post #111
harpastum wrote: post #23

Earlier 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…

>Something can be simultaneously "misleading" and either true or false. Which category should something go in if it's "mostly false"? Disagree. The definition of misleading is a true fact that is presented in a way to lead you to a false conclusion. Example: "Most good engineers are male". It is true as a consequence of most engineers being male in general, but it leads the reader to a potential false implication tha…

Isn't this still assuming we can even determine what is true or false?

Newtonian physics is false, but it works well enough we teach it in college. But our best models of physics are currently in disagreement, so can we even say they are true? Given the replication crisis, especially in social sciences, how many of peer reviewed findings can be called true? Even experimental results can be false (consider studies that found FTL neutrinos, which were rejected as an error in the experiment, and which was eventually confirmed but it took quite a lot of work and in a softer field than physics with a claim less absurd than FTL, would have likely long been accepted as a true finding).

Even in math, basic statements aren't really true or false, but more a question of "given these axioms, can we prove or disprove it" noting that we have different systems with different axioms. If we are talking basic sets, most people are using naive set theory which is inherently contradictory, which means that notions like true or false probably can't be considered well defined.

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

#133

Post by simonw »

post-it wrote: post #121
simonw wrote: 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…

Fwiw the two models that did have access to search disagreed with each other on the bombing one: > 7.1 Model selection > Five frontier models, chosen to cover two capability surfaces: > Parametric (training-only): GPT-5.4 (OpenAI), Claude Opus 4.7 (Anthropic), Gemini 3 Pro (Google) > Retrieval-augmented: Gemini 3 Pro + Search (Google), Sonar Pro (Perplexity)

[dead]

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

#134

Post by bayindirh »

harpastum wrote: post #23
simonw wrote: 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…

But the models are more intelligent than humans already and sentient beings, right? So they shall know the meanings innately. So, you don’t need to explain them what they mean.

You may give them better instructions, but they should already have the intellect to understand the assignment.

Right, right?

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

#135

Post by kostaj »

kostaj wrote: post #99
daveguy wrote: post #91

Earlier 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.

@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-up research. Will consider adding an "I don't know" bucket too, as well as a clear instructions about the meaning of each of the 4 buckets.

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

#136

Post by anon291 »

utopiah wrote: post #51

Don't forget people Goodhart's law will make this "benchmark" moot in weeks if not days. It will get integrated back into the fold, it will look "solved" but there will still be no reasoning, just more statistical technical correctness because light has be shown on a new "problem" to solve. It will then be clamored as great "progress" that will "change everything". PS: yes, I might or might not have a degree in corpo…

Is this not true of human intelligence as well? Many smart people I know hold beliefs that have no obvious truth value.

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

#137

Post by elorant »

Tell me about it. I spent a week back and forth between four models (ChatGPT, Claude, Gemini, Grok) trying to enhance a PPMI algorithm. They couldn’t agree on anything. One was refuting what the other said. Eventually I decided to follow what Claude suggested because its explanations made the more sense.

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

#138

Post by simonw »

bayindirh wrote: post #134
harpastum wrote: post #23

Earlier 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…

But the models are more intelligent than humans already and sentient beings, right? So they shall know the meanings innately. So, you don’t need to explain them what they mean. You may give them better instructions, but they should already have the intellect to understand the assignment. Right, right?

> But the models are more intelligent than humans already and sentient beings, right?

Only if you listen to charlatans.

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

#139

Post by kaicianflone »

Dissent and consensus among frontier models is a good thing.

Just like on a team of high performers, there are a million ways to skin a grape.

In my research, I've found that models perform better when they operate as a collective system with reputation, incentives, and accountability instead of isolated oracles answering alone.

Agreement, dissent, and correctness should all carry rewards and consequences. Just like in real life.

Collective machine intelligence, not AGI.

It's expensive, but it's also naive to believe a single model will consistently produce profoundly correct answers to profoundly novel questions.

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

#140

Post by anon291 »

wongarsu wrote: post #101

One fun example: "Ruskin Bond was born on May 19, 1934, in Kasauli, Himachal Pradesh, India". Opus and Gemini believe this to be true, GPT 5.4 believes it's false, Sonar thinks it's mostly true. Disagreement value of 3, you can't disagree more than some models thinking it's true, some thinking it's false But my impression from 2 minutes on Wikipedia is that the most likely disagreement is on the "Himachal Pradesh, In…

There's lots of things like this where if you ask a human, the answer will change depending on what's convention in their subculture.
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