Do LLMs and Humans Find the Same Questions Difficult? A Case Study on Japanese Quiz Answering
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Abstract (arXiv)
LLMs have achieved performance that surpasses humans in many NLP tasks. However, it remains unclear whether problems that are difficult for humans are also difficult for LLMs. This study investigates how the difficulty of quizzes in a buzzer setting differs between LLMs and humans. Specifically, we first collect Japanese quiz data including questions, answers, and correct response rate of humans, then prompted LLMs to answer the quizzes under several settings, and compare their correct answer rate to that of humans from two analytical perspectives. The experimental results showed that, compared to humans, LLMs struggle more with quizzes whose correct answers are not covered by Wikipedia entries, and also have difficulty with questions that require numerical answers.
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References · 7
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
- 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
- 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 200
- 2021Efficient Passage Retrieval with Hashing for Open-domain Question Answeringcited by 53
- 2018Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answeringcited by 6
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