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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

#81

Post by entrope »

jstummbillig wrote: post #65
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…

It's all fairly lazy to a degree that is mildly confusing. I also feel this among other issues would have become obvious if they had bothered to include a human fact checker baseline (i.e. asked multiple human fact checkers the same questions).

I do not think it is "lazy". Those labels are ones that human fact-checkers have been using for a decade or more. I think those human fact-checkers use those terms knowing full well that there is overlap and ambiguity between them. So I think this study ends up mixing three effects: how LLMs interpret the claims as statements about the world, how LLMs reduce that to a four-category judgment, and the inherent ambiguities of those labels as natural language. It's a quantification of those three factors combined, but not powerful enough to distinguish their relative sizes.

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

#82

Post by afavour »

jerf wrote: post #48
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…

This seems like another case where the models are acting like humans. Assuming they were not allowed to search the web, I wouldn't expect the models to necessarily have detailed information about all of these things directly in their training set. As large as they are, they are only so large, and they only have so much room for "information storage" in them, and there's a lot more things they need to fit into their n…

While I agree with what you’re saying the typical AI agent doesn’t say “I’m not totally sure about this, should I search the web?”. It often just spits out a reply based on its knowledge.

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

#83

Post by dfxm12 »

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…

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".

If you argue this, you would be arguing against reality and the English language so as to not upset AI. It's important to understand that AI is very much fallible.

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

#84

Post by WhitneyLand »

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…

So in other words if the research had tried to assign a severity to the mistakes models made the entire paper may collapse as uninteresting?

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

#85

Post by [deleted] »

kostaj wrote: post #60
ecshafer wrote: post #52

Earlier quoted context omitted.

These "Facts" are interesting. "Neptune Deep will start delivering natural gas in 2027." for example is not a fact, its a prediction. "On May 18, 2026, Ukraine carried out a drone attack on Moscow, Russia." is less of a fact and more of a litmus test for which sources of information you trust.

Indeed. Real-world claims are somewhat messy. Some of the standard benchmarks, e.g. the questions in AVeriTeC, share similar characteristics.

[deleted]

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

#86

Post by 6stringmerc »

Could be an interesting angle for cross-referencing with US jury verdicts, not that the objective True/False issue is concrete, but in the reality that flawed reasoning is endemic to our species. Systems designed and built by humans inherently have flaws in their DNA which take generations to sort out, if ever.

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

#87

Post by zug_zug »

proofofcontempt wrote: post #28

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.

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

#88

Post by embedding-shape »

kostaj wrote: post #36
embedding-shape wrote: post #21

Earlier quoted context omitted.

So it's not a secret, why you don't add this upfront to the report? The report itself is even about LLMs, makes a lot of sense to disclose your usage of them for writing the report, especially when you're presenting evidence that boils down to LLMs being infallible.

It's an omission on my side. Will add in the next version.

I think you might be able to edit the website to add this, even if you aren't willing to make the report a bit more honest up front.

I'm sure you realize that this website/article will now be sent around to a lot of people, many who don't realize exactly how this was written, because they don't read HN comments, they only skim the page contents, and I think most would (incorrectly) assume a report about infallible LLMs to not be written by LLMs, especially when the authors are the same ones who made the report itself.

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

#89

Post by wat10000 »

roxolotl wrote: post #79
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…

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.

Genius level intelligence will tell you to get lost with your "no explanations" nonsense and tell you why those categories don't make sense and why the question doesn't fit neatly into your boxes.

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

#90

Post by john_strinlai »

between 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"

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