mlsanityData to 13 Oct 2026

HumanEval on Latest GPT Models -- 2024

arXiv 2402.14852 ↗First posted 20 Feb 2024cs.CL cs.AI cs.LGcited by 7

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Abstract (arXiv)

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 .

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

7 of 13 references.

References · 7

  1. 2017Attention Is All You Needcited by 60,106
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,376
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,205
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,181
  5. 2023Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?cited by 54
  6. 2023Hierarchical Neural Program Synthesiscited by 7
  7. 2023Enhancing Robot Program Synthesis Through Environmental Contextcited by 3

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.