title: Learning Task Decomposition to Assist Humans in Competitive Programming
summary: 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.
paper_authors._id: 66b63d152174fa1446c304a7
paper_authors.name: Jiaxin Wen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T12:58:40.310Z
paper_authors._id: 5fd2bfccaa1f2d0285acf71d
paper_authors.avatarUrl: /avatars/1bb42f0af114299d8df43b26f2a296e9.svg
paper_authors.fullname: Jiaxin Wen
paper_authors.type: user
paper_authors.user: jiaxin-wen
paper_authors._id: 66b63d152174fa1446c304a8
paper_authors.name: Ruiqi Zhong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T13:01:55.041Z
paper_authors._id: 5eae5de74e876668a0c37779
paper_authors.avatarUrl: /avatars/cf04ab4832c6565ed085dec12fcae188.svg
paper_authors.fullname: Ruiqi Zhong
paper_authors.type: user
paper_authors.user: ruiqi-zhong
paper_authors._id: 66b63d152174fa1446c304a9
paper_authors.name: Pei Ke
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T13:02:15.034Z
paper_authors._id: 606c28fff3b1db0eeac26495
paper_authors.avatarUrl: /avatars/edad72f14661a06367f360b7fedf3e1b.svg
paper_authors.fullname: Pei Ke
paper_authors.type: user
paper_authors.user: kepei1106
paper_authors._id: 66b63d152174fa1446c304aa
paper_authors.name: Zhihong Shao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T13:02:30.632Z
paper_authors._id: 65db64f8b62d242ed8711701
paper_authors.avatarUrl: /avatars/753e9f980eb6786c6b53b2f1becbf745.svg
paper_authors.fullname: Zhihong Shao
paper_authors.type: user
paper_authors.user: ZhihongShao
paper_authors._id: 66b63d152174fa1446c304ab
paper_authors.name: Hongning Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T13:02:36.342Z
paper_authors._id: 62ec23fcbd19e355478fc584
paper_authors.avatarUrl: /avatars/d7c567ef5f20bb3b9905cb5015d11e12.svg
paper_authors.fullname: Hongning Wang
paper_authors.type: user
paper_authors.user: howang
paper_authors._id: 66b63d152174fa1446c304ac
paper_authors.name: Minlie Huang
paper_title: Learning Task Decomposition to Assist Humans in Competitive Programming
paper_summary: 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.
paper_ai_keywords: language models (LMs) task decomposition assistive value (AssistV) in-context examples