Learning Task Decomposition to Assist Humans in Competitive Programming
Authors: name: Jiaxin Wen, name: Ruiqi Zhong, name: Pei Ke, name: Zhihong Shao, name: Hongning Wang, name: Minlie Huang

When using language models (LMs) to solve complex problems, humans might
struggle to understand the LM-generated solutions and repair the flawed ones.
To assist humans in repairing them, we propose to automatically decompose
complex solutions into multiple simpler pieces that correspond to specific
subtasks. We introduce a novel objective for learning task decomposition,
termed assistive value (AssistV), which measures the feasibility and speed for
humans to repair the decomposed solution. We collect a dataset of human repair
experiences on different decomposed solutions. Utilizing the collected data as
in-context examples, we then learn to critique, refine, and rank decomposed
solutions to improve AssistV. We validate our method under competitive
programming problems: under 177 hours of human study, our method enables
non-experts to solve 33.3\% more problems, speeds them up by 3.3x, and empowers
them to match unassisted experts.