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[2405.10385] Augmenting Lateral Thinking in Language Models with Humor and Riddle Data for the BRAINTEASER Task
Summary
This paper explores enhancing language models' lateral thinking abilities by integrating humor and riddle datasets for the BRAINTEASER task, achieving notable accuracy improvements.
Why It Matters
The study addresses the underexplored area of lateral thinking in NLP, demonstrating how humor and riddles can improve language model performance. This has implications for developing more sophisticated AI systems capable of creative reasoning, which is vital for applications in education, entertainment, and problem-solving.
Key Takeaways
- The BRAINTEASER task challenges models to perform creative reasoning.
- Augmenting training data with humor and riddles significantly boosts performance.
- Framing tasks as multiple-choice improves accuracy by 10 points.
- Sentence-level puzzles are easier for models compared to word-level puzzles.
- The study ranks the proposed model among the top competitors in the task.
Computer Science > Computation and Language arXiv:2405.10385 (cs) [Submitted on 16 May 2024 (v1), last revised 23 Feb 2026 (this version, v3)] Title:Augmenting Lateral Thinking in Language Models with Humor and Riddle Data for the BRAINTEASER Task Authors:Mina Ghashami, Soumya Smruti Mishra View a PDF of the paper titled Augmenting Lateral Thinking in Language Models with Humor and Riddle Data for the BRAINTEASER Task, by Mina Ghashami and 1 other authors View PDF HTML (experimental) Abstract:The SemEval 2024 BRAINTEASER task challenges language models to perform lateral thinking -- a form of creative, non-linear reasoning that remains underexplored in NLP. The task comprises two subtasks, Sentence Puzzle and Word Puzzle, requiring models to defy conventional commonsense associations. We present a system that fine-tunes DeBERTaV3 using HuggingFace's AutoModelForMultipleChoice architecture. We augment the provided training data with two additional sources: (1) a humor-style question-answering dataset generated via GPT-4 prompting, and (2) the RiddleSense dataset. This data augmentation strategy is motivated by the observation that humor and riddles share the lateral reasoning structure required by the task. Our best system achieves 92.5\% overall accuracy on the Sentence Puzzle subtask and 80.2\% on the Word Puzzle subtask, ranking 6th out of 31 teams and 10th out of 23 teams, respectively. We further show that the choice of task formulation matters: framing the problem as ...