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SOTA
Codegenerierung
Code Generation On Apps
Code Generation On Apps
Metriken
Competition Pass@1
Interview Pass@1
Introductory Pass@1
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Competition Pass@1
Interview Pass@1
Introductory Pass@1
Paper Title
LPW (GPT-4o)
34.8
65.2
87.2
Planning-Driven Programming: A Large Language Model Programming Workflow
MoTCoder-32B-V1.5
27.84
44.49
68.44
MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks
MoTCoder-7B-V1.5
21.18
32.63
54.26
MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks
code-davinci-002 175B (CodeT)
6.2%
14.3%
47.3%
CodeT: Code Generation with Generated Tests
deepseek-ai/deepseek-coder-6.7b-instruct
11.09
19.70
33.80
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
code-davinci-002 175B
-
-
31.92
CodeT: Code Generation with Generated Tests
CodeChain+WizardCoder-15b
2.5%
6.4%
29.3%
CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules
WizardCoder-15b
3.75
7.49
26.29
CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules
CodeSim (GPT4)
0.81
4.21
26.04
CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging
CodeRL+CodeT5
33.3
13.5
20
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
GPT-J 6B (Finetuned)
0.69%
1.80%
6.77%
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
Codex 12B (Raw)
0.50%
1.00%
5.60%
Evaluating Large Language Models Trained on Code
GPT-Neo 2.7B (Finetuned)
0.02%
0.14%
4.14%
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
GPT-Neo 2.7B
0.00%
0.57%
3.90%
Measuring Coding Challenge Competence With APPS
GPT2 1.5B (Finetuned)
0.00%
0.57%
3.90%
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
MapCoder APPS-150-cherrypicked (GPT-4)
0.00%
0.70%
1.30%
MapCoder: Multi-Agent Code Generation for Competitive Problem Solving
AlphaCode 1B
-
-
-
Competition-Level Code Generation with AlphaCode
AlphaCode 1B Filtered from 50000
-
-
-
Competition-Level Code Generation with AlphaCode
0 of 18 row(s) selected.
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Code Generation On Apps | SOTA | HyperAI