title: AutoCoder: Enhancing Code Large Language Model with
    AIEV-Instruct
summary: We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo
  (April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test
  (90.9% vs. 90.2%). In addition, AutoCoder offers a more
  versatile code interpreter compared to GPT-4 Turbo and GPT-4o. It's code
  interpreter can install external packages instead of limiting to built-in
  packages. AutoCoder's training data is a multi-turn dialogue dataset created by
  a system combining agent interaction and external code execution verification,
  a method we term \textsc{AIEV-Instruct} (Instruction Tuning with
  Agent-Interaction and Execution-Verified). Compared to previous large-scale
  code dataset generation methods, AIEV-Instruct reduces dependence on
  proprietary large models and provides execution-validated code dataset. The
  code and the demo video is available in
  https://github.com/bin123apple/AutoCoder.
paper_authors._id: 6653eaa9fda461267c7aa31b
paper_authors.name: Bin Lei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-27T19:46:15.466Z
paper_authors._id: 648a2042e8bee533291da413
paper_authors.avatarUrl: /avatars/83b918ddd7e2130a1c72ae74606068dc.svg
paper_authors.fullname: Bin Lei
paper_authors.type: user
paper_authors.user: Bin12345
paper_authors._id: 6653eaa9fda461267c7aa31c
paper_authors.name: Yuchen Li
paper_authors._id: 6653eaa9fda461267c7aa31d
paper_authors.name: Qiuwu Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-27T10:03:48.246Z
paper_authors._id: 63ae84873e98f3ec1945d09d
paper_authors.avatarUrl: /avatars/26c6cf2c824ec777c55e56947809d7c7.svg
paper_authors.fullname: qiuwuchen
paper_authors.type: user
paper_authors.user: qiuwu
paper_title: AutoCoder: Enhancing Code Large Language Model with
    AIEV-Instruct
paper_summary: We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo
  (April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test
  (90.9% vs. 90.2%). In addition, AutoCoder offers a more
  versatile code interpreter compared to GPT-4 Turbo and GPT-4o. It's code
  interpreter can install external packages instead of limiting to built-in
  packages. AutoCoder's training data is a multi-turn dialogue dataset created by
  a system combining agent interaction and external code execution verification,
  a method we term \textsc{AIEV-Instruct} (Instruction Tuning with
  Agent-Interaction and Execution-Verified). Compared to previous large-scale
  code dataset generation methods, AIEV-Instruct reduces dependence on
  proprietary large models and provides execution-validated code dataset. The
  code and the demo video is available in
  https://github.com/bin123apple/AutoCoder.
paper_ai_summary: AutoCoder, a Large Language Model surpassing GPT-4 Turbo and GPT-4o, uses a novel training method \textsc{AIEV-Instruct} to enhance code interpretation and execution validation.
paper_ai_keywords: Large Language Model GPT-4 Turbo GPT-4o Human Eval benchmark pass@1 code interpreter external packages multi-turn dialogue dataset AIEV-Instruct Instruction Tuning Agent-Interaction Execution-Verified