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Disagreement among frontier LLMs on real-world fact-checks
331–340 of 377 posts
Re: Disagreement among frontier LLMs on real-world fact-checks
#332Earlier quoted context omitted.
This made me chuckle. This brings up a very valid point, though. So many _humans_ can't agree on what the facts are these days. It seems to be getting worse. Not sure of the solution.
> So many _humans_ can't agree on what the facts are these days. Ask ten people what "knowledge" is, and they'll come up with ten different answers. Go back 10, 50 or 100 years and humanity struggled with exactly the same issue for so long time. There is even an entire field of study literally just for trying to figure out what "knowledge" is: https://en.wikipedia.org/wiki/Epistemology
And on top of that, that entire field has not reached a consensus answer, and
answers that have been proposed have been shown to be flawed.
Re: Disagreement among frontier LLMs on real-world fact-checks
#333Here'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…
Everything you wrote is very goes criticism and I would love to stress the main takeaway: you’re not just testing the model but the prompt and harness as well.
An excellent follow up study would be to change the prompt and compare the answers. You might find out that the models are good and the prompt is bad.
…And so the main corollary is: build evals for anything you deploy in production; benchmark and monitor, or face the consequences.
Re: Disagreement among frontier LLMs on real-world fact-checks
#334[flagged]
If your quote were true, then by the Randian logic of the people who make such claims, they must deserve to do so and you shouldn't have any issue with it.
But your quote certainly isn't true if you're actually talking about "the world". For example, Japan and China are the two largest holders of US Treasury bonds. China controls roughly 50% of the the contracted construction market in Africa. These are just examples of the sort of thing you'd need to take into account in trying to justify your silly racist claim.
Re: Disagreement among frontier LLMs on real-world fact-checks
#335Here'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…
Cherry-picking is fun but most of them are real, verifiable facts that the models get... straight up wrong.
> 3c24b5fe "Debian Security Advisory DSA-180-1 describes a buffer overflow vulnerability involving Cyrus SASL usernames." TRUE Mostly True FALSE FALSE FALSE
This is false: https://lwn.net/Articles/13296/
> 801cb8c1 "Equal Measures 2030's 2024 SDG Gender Index provides a downloadable dataset that includes a field labeled 'required annual change'." TRUE Mostly True TRUE FALSE FALSE
This is false: https://equalmeasures2030.org/2024-sdg-gender-index/
This is the "confidently wrong" problem, and the reason that LLMs won't ever be taken seriously for anything but a few niche use-cases (like generating slop-code and pumping out marketing materials), where being wrong isn't the end of the world. Akin to how speech-to-text is wrong often enough that, while being a fun novelty, you don't see business units writing reports in Word using STT.
I would encourage everyone to skim through the real 1000-question dataset: https://lenz.io/research/llm-disagreement/data.csv
Re: Disagreement among frontier LLMs on real-world fact-checks
#336Here'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? [1]: https://en.wikipedia.org/wik…
Weird, this page says 80%
https://en.wikipedia.org/wiki/Almond_cultivation_in_Californ...
Re: Disagreement among frontier LLMs on real-world fact-checks
#337Earlier quoted context omitted.
> 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.
how is that misleading if it's a fact, it's only misleading if you presume to know the reaction or intent behind making such a claim, and without context we should be extremely careful in making such presumptions.
It's misleading because a single murder in this case is not statistically significant, but phrasing it using probabilistic terminology (i.e. percentages) obscures that fact and implies that you have enough data for the probabilistic language to be relevant.
Choosing to use percentages when there is a countable or small amount of data is typically misleading, even though it is "technically" true. In fact, a misleading statement is almost always something that is technically a fact.
Re: Disagreement among frontier LLMs on real-world fact-checks
#338The CVS file with the raw data is a source of joy. My favorite is this:
claim: Artificial intelligence will cause widespread job loss among software engineers.
All 5 LLM's agree that the claim is misleading & wrong.
Re: Disagreement among frontier LLMs on real-world fact-checks
#339Earlier quoted context omitted.
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…
Can you share what you mean by this? > when using dedicated AI resources that I'm paying for Are there API-based search providers that structure their results differently?
I mean as opposed to the ones the search engines provide for "free".
I've lost count of the number of times they've cited a link and it said the exact opposite of what they "summarized" it as saying. At this point I wouldn't be surprised that search LLM got exactly what you see on the search results (that is, didn't actually load the web page) and then just makes a plausible guess based on that. But those "free" AIs are definitely not the state of the art of what you can get from one that is actually budgeted to think and actually fetch web resources. If you pay for an AI it does much better. I'm yet to catch one out in a blatant contradiction, unlike the free search engines which I've caught multiple times. YMMV, of course, because we all have different searches and experiences and we may have different thresholds for what constitutes an "error".
Re: Disagreement among frontier LLMs on real-world fact-checks
#340even this is ai output