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HumanEval on Latest GPT Models -- 2024

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arxiv 2402.14852 v1 pith:UY7PUD3D submitted 2024-02-20 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords modelslanguagesynthesiscodehumanevallatestprogramsignificantly
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In 2023, we are using the latest models of GPT-4 to advance program synthesis. The large language models have significantly improved the state-of-the-art for this purpose. To make these advancements more accessible, we have created a repository that connects these models to Huamn Eval. This dataset was initally developed to be used with a language model called CODEGEN on natural and programming language data. The utility of these trained models is showcased by demonstrating their competitive performance in zero-shot Python code generation on HumanEval tasks compared to previous state-of-the-art solutions. Additionally, this gives way to developing more multi-step paradigm synthesis. This benchmark features 160 diverse problem sets factorized into multistep prompts that our analysis shows significantly improves program synthesis over single-turn inputs. All code is open source at https://github.com/daniel442li/gpt-human-eval .

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  1. AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models

    cs.SE 2025-05 conditional novelty 6.0 of 10

    An automated, execution-based benchmark of 1,325 Google Earth Engine unit tests shows 18 LLMs scoring between 31.40% and 71.55% pass@1, with parameter-knowledge errors the dominant failure mode.

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