RAGthoven at SemEval-2026 Task 1: A Multi-Stage Pipeline Walks Into a Benchmark and Barely Clears the Bar
Year: 2026
Type: conference-paper
Abstract: We present RAGTHOVEN, our system for SemEval-2026 Task 1 (MuWaHaHa), Subtask A (multilingual constrained humor generation in English, Spanish, and Chinese).RAGTHOVEN decomposes creative text generation into a multi-stage large language model (LLM) pipeline (Planner, Writer, Reflector, Judge) grounded in computational humor theories (Benign Violation Theory, Script-based Semantic Theory of Humor) and iteratively refined through prompt engineering across ten experiments.In our final configuration, we augment the Planner with retrieval-augmented generation (RAG) from a curated joke corpus, seeding generation with diverse joke mechanisms.We additionally explore an agentic variant that exposes the same four pipeline stages as toolcalling agents orchestrated by a model loop with a CONSTRAINTAUDIT checker; while it achieves full constraint compliance, human pairwise evaluation did not reveal a significant quality advantage over the simpler non-agentic baseline.RAGTHOVEN achieves Rank 1 in all three languages, with the strongest result in Spanish (Elo 1182, 42 points above the Gemini 2.5 Flash baseline).However, while the system leads in raw Elo in Spanish, it shares Rank 1 with the baseline in all three languages due to overlapping confidence intervals; in English and Chinese the gap narrows further, suggesting that elaborate multi-stage prompt engineering may offer diminishing returns once a strong frontier model is in the loop.
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166804173