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What if you could measure humor?Well we've trained a model on our own dataset of ~50k human ratings to detect what jokes people find funniest. We know it's part objective, part subjective component. Subjective is out of our depth for now hahaThe main results: Fable 5 is funniest - beating the average model 67% of the time, with GPT 4o last at 17%.Other findings: - The models never refused to try, even with dark prompts - Thinking longer has a slight benefit - Absurdness correlates negatively with joke qualitySome methodology notes: - We benchmarked our model against the human majority and it agreed 72% of the time in a blind sample test. - We had 51 US adults rate the jokes, each blind to the models, with joke order randomized, and quality checked for attention and speed. - To rate some yourself visit https://pair.laugh.soThe full benchmark here:https://laugh.so/benchmarkAm taking requests if there's more research you want to see! Cheers
Comments (4)
measuring humor objectively is such a bold problem to tackle
50k ratings from your own dataset feels like a narrow way to measure humor
50k human ratings is good training data for humor detection.
what architecture and how do you handle subjective label variance
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