221–230 of 377 posts
I 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.
Not sure I'm understanding this. The models are asked to evaluate the truth of random claims out of their own head (except for Gemini with search grounding)? Isn't it exactly the same as asking people to play any quiz game and then rating them as "they disagree n% of the time"? The output buckets are also pretty questionable- the difference between "True" and "Mostly true" is pretty fuzzy. Is this marked as a "disagr…
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…
Not on objective truth though. That's how you get misinformation.
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…
Funny timing. I've been working on a prediction market orchestration that runs Claude and a few others over Polymarket/Kalshi. The models are NOT unanimous. At all, really. I spent about a month convinced that I could just run all five and take majority vote. Eventually I pivoted to a chaining approach where I benchmark areas each model excels, and settled on more like a graph-like architecture where outputs get spli…
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…
model total_claims hedged_count hedged_pct
claude-opus-4-7 1000 451 45.1
sonar-pro 1000 391 39.1
gpt-5.4 1000 277 27.7
gemini-3-retrieval 1000 129 12.9
gemini-3-pro 1000 60 6.0
https://lite.datasette.io/?csv=https%3A%2F%2Fstatic.simonwil...
More interesting part probably worth highlighting: The SAME model won't always return the same output when prompted with the same fact check. You ask a human 1000 times a fact check question, they say the same answer 1000 times. You ask an LLM the same question a 1000 times, your results could vary significantly. Humans work based on the Metamemory (knowing what they know), while LLMs are picking from statistical pro…
I have labeled datasets with a human team and shown the same task to the same user on a different day, and they answered differently. Of course, they are usually consistent with themselves most of the time but not always.
Earlier quoted context omitted.
Yes, the labels are weird. Most misleading statements are true. Any "mostly true" statement is false. I suspect the intention was "Factually true, and no gotchas exist", "technically not true, but so close to the truth that the difference doesn't matter", "technically true, but there are major gotchas" and "factually false and not even close". But that's not what they specified
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…
"Extraterrestrial life exists somewhere in the universe." GPT-5.4: Misleading Opus 4.7: Misleading Gemini 3: FALSE Gemini 3 (Retrieval): FALSE Sonar Pro: FALSE It's a weird fact claim, because the ground truth is "nobody knows for sure" and that's not one of the available options.
Then again maybe that’s why I’m an atheist, not an agnostic?
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.
"Extraterrestrial life exists somewhere in the universe." GPT-5.4: Misleading Opus 4.7: Misleading Gemini 3: FALSE Gemini 3 (Retrieval): FALSE Sonar Pro: FALSE It's a weird fact claim, because the ground truth is "nobody knows for sure" and that's not one of the available options.