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An LLM-Based Automatic Selection of High-Difficulty Questions Using the Swiss Tournament Format

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(2), pp.31~42
  • DOI : 10.9708/jksci.2026.31.02.031
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 12, 2025
  • Accepted : January 28, 2026
  • Published : February 27, 2026

Jooeun Lee 1 Minseob Song 1 Namgyu Kim 1

1국민대학교

Accredited

ABSTRACT

With the advancement of large language models(LLMs), in-context learning has become a key approach, driving research on prompting techniques. Among them, few-shot Chain-of-Thought(CoT) prompting, which induces explicit reasoning, shows strong performance but depends on example composition. While prior work focused on diversity or uncertainty in example selection, difficulty-based approaches remain underexplored. This study proposes a method to identify high-difficulty questions by combining pairwise difficulty comparisons conducted by an LLM with a Swiss tournament structure, constructing few-shot CoT exemplars with human reasoning annotations. Experiments on 1,319 GSM8K problems show that the proposed method outperforms random, uncertainty-based, and direct difficulty evaluation approaches by 2.12%p, 1.36%p, and 10.16%p, respectively.

Citation status

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