i built a benchmark to test whether LLMs can understand and create jokes

Reddit r/artificial Tools

Summary

A developer built a benchmark called lolbench to evaluate LLMs on understanding and creating jokes, revealing models are strong at explaining real jokes but struggle with failed ones, and incorporating human voting for preference testing.

lolbench - LLMs take three tests: - explain why jokes work (or don't) - write jokes under shared premises - predict which jokes humans prefer The finding so far that surprised me: every model aces explaining real jokes (95%+) but drops hard on explaining why a failed joke fails (81–92%). the part I need humans for: the vote booth. two clanker jokes, you pick blind, then it reveals the models + whether you agreed with thousands of other voters. ~30 seconds a ballot + a laugh (hopefully) https://lolbench.lol/ happy to answer anything about the eval system and open to any sort of feedback!
Original Article

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