{"paper":{"title":"HumorRank: A Tournament-Based Leaderboard for Evaluating Humor Generation in Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"HumorRank ranks language models on humor generation through automated joke tournaments that reveal skill in comedic mechanisms over model size.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Edward Ajayi, Prasenjit Mitra","submitted_at":"2026-03-31T18:54:15Z","abstract_excerpt":"Humor remains difficult to evaluate in large language models (LLMs) because what makes a response funny is subjective, comparative, and shaped by interacting comedic mechanisms rather than a single scalar property. Existing humor evaluation protocols therefore tend to produce isolated scores or task-specific judgments that are difficult to compare across models. We introduce HumorRank, a tournament-based framework for ranking textual humor generation through theory-grounded pairwise preference judgments. Across SemEval-2026 MWAHAHA and Humor Transfer Bench, HumorRank evaluates nine proprietary"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our results demonstrate that HumorRank yields statistically grounded model stratifications, showing that humor quality is driven by mastery of comedic mechanisms rather than model scale alone.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Automated pairwise judgments grounded in the General Theory of Verbal Humor accurately reflect true humor quality without systematic bias or the need for human validation.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"HumorRank ranks nine LLMs on textual humor using GTVH-grounded pairwise tournaments and Adaptive Swiss aggregation on the SemEval-2026 MWAHAHA dataset, finding that comedic mechanism mastery matters more than scale.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"HumorRank ranks language models on humor generation through automated joke tournaments that reveal skill in comedic mechanisms over model size.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"7811f44e58e8d0fb363945807edb4bb5d173d04a533c0200ac2c42c0aae3cfaa"},"source":{"id":"2604.19786","kind":"arxiv","version":2},"verdict":{"id":"eb99c771-efe4-47b5-943c-7dfc64a517eb","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T23:01:25.712854Z","strongest_claim":"Our results demonstrate that HumorRank yields statistically grounded model stratifications, showing that humor quality is driven by mastery of comedic mechanisms rather than model scale alone.","one_line_summary":"HumorRank ranks nine LLMs on textual humor using GTVH-grounded pairwise tournaments and Adaptive Swiss aggregation on the SemEval-2026 MWAHAHA dataset, finding that comedic mechanism mastery matters more than scale.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Automated pairwise judgments grounded in the General Theory of Verbal Humor accurately reflect true humor quality without systematic bias or the need for human validation.","pith_extraction_headline":"HumorRank ranks language models on humor generation through automated joke tournaments that reveal skill in comedic mechanisms over model size."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.19786/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"ecec7f28dec845e7b315f057f3711c9c72b24384fc91583ca2b874ddfbd2cfe1"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}