• arxiv preprint - Learning Task Decomposition to Assist Humans in Competitive Programming

  • Aug 16 2024
  • Duración: 6 m
  • Podcast

arxiv preprint - Learning Task Decomposition to Assist Humans in Competitive Programming

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  • In this episode, we discuss Learning Task Decomposition to Assist Humans in Competitive Programming by Jiaxin Wen, Ruiqi Zhong, Pei Ke, Zhihong Shao, Hongning Wang, Minlie Huang. The paper presents a method to enhance human understanding and repair of language model (LM)-generated solutions by automatically breaking down complex solutions into simpler subtasks. They introduce a novel objective called assistive value (AssistV) to measure how easily humans can repair these subtasks and validate their method through a dataset of human repair experiences. The approach significantly improves the problem-solving ability and speed of non-experts in competitive programming, allowing them to solve more problems and match the performance of unassisted experts.
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