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LLMs struggle with creative riddle generation despite Gemini 3.1 Pro's strong performance

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the reasoning versus memorization capabilities of large language models (LLMs) when tasked with understanding and generating Chinese Xiehouyu riddles. Researchers created novel riddles to avoid data contamination and found that while frontier Chinese models showed a higher accuracy on existing riddles compared to English-centric models, suggesting larger Chinese datasets, their performance on novel riddles was still significantly lower than human experts. Gemini 3.1 Pro, however, showed a notable accuracy of 92.6% on novel riddles, surpassing human accuracy by 24%. Despite this, LLM-generated riddles received poorer ratings than human-created ones, indicating limitations in their creative abilities. AI

IMPACT Highlights potential overestimation of LLM reasoning and creativity due to data contamination, urging careful re-examination of model capabilities.

RANK_REASON Research paper published on arXiv evaluating LLM capabilities on a specific language task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with creative riddle generation despite Gemini 3.1 Pro's strong performance

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Research paper published on arXiv evaluating LLM capabilities on a specific language task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hai Hu, Siyuan Song, Chongtian Shao, Kejia Zhang, Tianjian Zhu, Xiaojing Zhao ·

    Reasoning or Memorization: Can LLMs Understand and Generate Chinese Xiehouyu Riddles?

    arXiv:2607.23440v1 Announce Type: cross Abstract: In this paper, we push the boundary of LLM reasoning by testing them in a Chinese language game, xiehouyu, with novel xiehouyu created by linguists that had not existed before to avoid data contamination. We use multiple-choice qu…

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