{"tstamp": 1722356200.3701, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "2719b38b30a44cb08b58c23cfaaac5fe", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Research improving code generation models using CommitPack and HumanEvalPack datasets.", "0_output": [["Research improving code generation models using CommitPack and HumanEvalPack datasets.", "Title: Self-Edit: Fault-Aware Code Editor for Code Generation\n\nAbstract: Large language models (LLMs) have demonstrated an impressive ability to generate codes on competitive programming tasks. However, with limited sample numbers, LLMs still suffer from poor accuracy. Inspired by the process of human programming, we propose a generate-and-edit approach named Self-Edit that utilizes execution results of the generated code from LLMs to improve the code quality on the competitive programming task. We execute the generated code on the example test case provided in the question and wrap execution results into a supplementary comment. Utilizing this comment as guidance, our fault-aware code editor is employed to correct errors in the generated code. We perform extensive evaluations across two competitive programming datasets with nine different LLMs. Compared to directly generating from LLMs, our approach can improve the average of pass@1 by 89\\% on APPS-dev, 31\\% on APPS-test, and 48\\% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. Compared to other post-processing methods, our method demonstrates superior accuracy and efficiency."]], "0_corpus": "arxiv", "1_conv_id": "9f392c55ce0242e699bcfa03f3da8923", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Research improving code generation models using CommitPack and HumanEvalPack datasets.", "1_output": [["Research improving code generation models using CommitPack and HumanEvalPack datasets.", "Title: Enhancement of the superconducting transition temperature in La2-xSrxCuO4 bilayers: Role of pairing and phase stiffness\n\nAbstract: The superconducting transition temperature, Tc, of bilayers comprising underdoped La2-xSrxCuO4 films capped by a thin heavily overdoped metallic La1.65Sr0.35CuO4 layer, is found to increase with respect to Tc of the bare underdoped films. The highest Tc is achieved for x = 0.12, close to the 'anomalous' 1/8 doping level, and exceeds that of the optimally-doped bare film. Our data suggest that the enhanced superconductivity is confined to the interface between the layers. We attribute the effect to a combination of the high pairing scale in the underdoped layer with an enhanced phase stiffness induced by the overdoped film."]], "1_corpus": "arxiv"}