Watch the disagreements in real time via refinement pipeline on the results page pingpongit.com
Disagreement among frontier LLMs on real-world fact-checks
321–330 of 377 posts
Re: Disagreement among frontier LLMs on real-world fact-checks
#322This shouldn’t be surprising. Let’s start off with the obvious. What does “real-world fact-check claims” mean? So we’re using the same list of “fact check claims” on each model. The problem is (unless I’m missing it) the authors aren’t exposing the list of 1K questions they used in the experiment. That’s a huge problem. Are the authors assuming the 1K claims they used are “provably true”? If so, that’s a huge bias, and opens up a philosophical debate about what it a fact? Or what’s makes something true/ false?
As Marc Andreessen puts it: a particular domain is either explicitly “provable” or not “provable”. Provable domains include math, physics, chemistry, biology, engineering, even code. That not be the whole list, but everything else is essentially “unprovable”. At least as far as a language model is concerned. They are questions that require a human value judgement. Politics are an obvious example. So back to the “1K fact check claims“. How many of these are political, or current events questions? How many are STEM questions that can be laid out in a formal proof?
Models can be trained to answer either way on claims that require a value judgement, but that’s obviously not beneficial to anyone except who controls the model. If the expectation is that all these frontier models should answer the same way on value judgement questions, then that’s never going to happen. What the models ARE good at though is breaking down the nuances of a topic and arguing both sides. This is how these tools should be used, as a way to analyze the claim and let us humans in the end make our own value judgement. If you’re trusting the model to make the value judgement for you and just accept it as a fact, then you are entering a a very dangerous territory.
Re: Disagreement among frontier LLMs on real-world fact-checks
#323Very interesting tool, but it's biased and not neutral from the get go, because I explicitly formulated claim in neutral way, but it automatically rewrote it to be western/wikipedia POV and then immediately proceeded to verify it.
original neutral:
US DEPT OF DEFENSE/DNAVFAC planned renovations to School #05 in Sevastopol, Crimea in 2013 before Crimea became part of Russia in 2014
The United States Department of Defense, via the Naval Facilities Engineering Command (NAVFAC), planned renovations to School No. 5 in Sevastopol, Crimea in 2013, before Russia annexed Crimea in 2014.
And the follow up
The phrasing "Crimea became part of Russia" is more neutral than the phrasing "Russia annexed Crimea."
Yeah, so my personal conclusion that this tool is garbage, it checks western/US allied only LLM providers, that in turn search only for western/US allied sources/documents like BBC/NATO and result is what it is.
Re: Disagreement among frontier LLMs on real-world fact-checks
#324Re: Disagreement among frontier LLMs on real-world fact-checks
#325[flagged]
Re: Disagreement among frontier LLMs on real-world fact-checks
#326Here'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 think the headline result is the cross product table.
Gemini Pro + Search agreed with Gemini Pro w/o Search 75% of the time, and with everybody else about 50% of the time. No other model had access to search.
So, search is not improving the quality of fact checking 75% of the time (probably a bad system prompt and/or bad fact checking queries), and if asked to flip a coin, then the models do.
Re: Disagreement among frontier LLMs on real-world fact-checks
#327My first reaction was: how dumb is AI still.
But ... real people would also reach that result. Some believe that vaccination can not induce protection (which objectively is incorrect).
Re: Disagreement among frontier LLMs on real-world fact-checks
#328Earlier quoted context omitted.
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.
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Re: Disagreement among frontier LLMs on real-world fact-checks
#329Earlier quoted context omitted.
Everything has inherited biases. Grok has explicit biases on top of its training set [^1]. [1] https://www.reddit.com/r/singularity/comments/1p22c89/people...
It’s part of the system prompt. It doesn’t constitute a bias in the model itself.
I agree with you. This doesn’t necessarily mean model bias but it exposes the attitude of the xAi team towards what they are trying to build.
It’s difficult to prove but it’s not hard to imagine they will/are trying to remove favorable views certain topics from their training set.
Re: Disagreement among frontier LLMs on real-world fact-checks
#330Earlier quoted context omitted.
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 LL…
fyi, "infallible" means never wrong, never failing, never making a mistake.