---
title: "lmfaoooo at SemEval-2026 Task 1: Humor Is an Audience."
description: "The authors also release a new set of human pairwise judgments from Humor Arena. 36 references, cited by 1 paper here."
authors: ["Alexey Tikhonov", "Alexey Ivanov"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.15/"
doi: "10.18653/v1/2026.semeval-1.15"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tikhonov-2026-lmfaoooo-semeval-2026-task-1/"
---
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# lmfaoooo at SemEval-2026 Task 1: Humor Is an Audience. Preference Modeling for Constrained Humor Generation

**Authors:** [Alexey Tikhonov](https://md.research.elamur.ai/free/en/authors/alexey-tikhonov/) and Alexey Ivanov · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.15/) · [PDF](https://aclanthology.org/2026.semeval-1.15.pdf) · [DOI 10.18653/v1/2026.semeval-1.15](https://doi.org/10.18653/v1/2026.semeval-1.15) · [OpenAlex](https://openalex.org/W7166800870)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The authors address constrained joke generation for SemEval-2026 Task 1 (MWAHAHA), where English, Spanish and Chinese jokes must satisfy constraints such as a news headline or required words, and are judged by humans in pairwise arena battles. They argue that modern LLMs can easily produce many candidate jokes. The harder problem is picking the one a given audience will find funniest, especially because human humor judgments are noisy and depend on the audience. Their system works in two stages. First, it generates a diverse candidate pool through multi-step association prompting, a model ensemble and tail-focused sampling. Second, it ranks the candidates with a preference model trained on human pairwise comparisons, not on absolute funniness ratings. This model relies on an interpretable "humor basis" of features that an LLM derives from differences between jokes. Across three datasets, pairwise feature-based models beat pointwise scoring and a plain LLM-judge baseline, and they transfer better across datasets. The system placed first in English and Chinese and second in Spanish. The authors also release a new set of human pairwise judgments from Humor Arena.

## Key points
- **New resource:** 2,543 pairwise joke comparisons collected in 391 Humor Arena sessions, each with one of four outcomes. Only win/loss outcomes are used for training. The authors also release prompts, candidate pools, features and rankings.
- **Generation:** about 50 candidates per instance from claude-sonnet-4, claude-opus-4.5 and GPT-5 at temperature 1.0. The pool is filtered by hard constraint checks and deduplicated with embeddings.
- **Humor basis:** LLM-generated "difference hints" are clustered with DP-means and then deduplicated, giving 17 features (e.g., Clear Punchline, Subverting Expectations). Final rankings are aggregated with Bradley–Terry/Elo models via Evalica.
- **10-fold CV accuracy (Reddit / ScaleAI / H.Arena):** pairwise models 83/64/77%, pointwise models 63/58/45%, NoBasis LLM-judge 41/32/37%. Cross-dataset transfer is partial and asymmetric but generally stronger for pairwise models.
- **MWAHAHA results (Elo):** 1st in English (1041), 1st in Chinese (1081), 2nd in Spanish (1091).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Alexey Tikhonov and Alexey Ivanov. 2026. lmfaoooo at SemEval-2026 Task 1: Humor Is an Audience. Preference Modeling for Constrained Humor Generation. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tikhonov2026lmfaoooo,
  title = {{lmfaoooo at SemEval-2026 Task 1: Humor Is an Audience. Preference Modeling for Constrained Humor Generation}},
  author = {Alexey Tikhonov and Alexey Ivanov},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.15},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.15}
}
```

- [← previous LLM-to-Speech: A Synthetic Data Pipeline for Training Dialectal Text-to-Speech Models](https://md.research.elamur.ai/free/en/papers/khamis-2026-llm-speech-synthetic-data-pipeline/)
- [next → LocuPrompt at SemEval-2026 Task 7: A Multilingual Prompting Framework for Cross-Cultural Everyday Knowledge in LLMs](https://md.research.elamur.ai/free/en/papers/ning-2026-locuprompt-semeval-2026-task-7/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/14/)

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*Updated 31 October 2026*