HumanEval on Latest GPT Models -- 2024
Not on the map. Why?
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
- 2017Attention Is All You Needcited by 60,106
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,376
- 2021Evaluating Large Language Models Trained on Codecited by 8,205
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,181
- 2023Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?cited by 54
- 2023Hierarchical Neural Program Synthesiscited by 7
- 2023Enhancing Robot Program Synthesis Through Environmental Contextcited by 3
Built on by
Citing papers per year: 2025: 3; 2026: 4.
7 papers.
- 2025SP-MoE: Speculative Decoding and Prefetching for Accelerating MoE-based Model Inferencecited by 12
- 2026Breaking Model Lock-in: Cost-Efficient Zero-Shot LLM Routing via a Universal Latent Spacecited by 3
- 2026From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequencescited by 3
- 2025AutoBench: Automating LLM Evaluation through Reciprocal Peer Assessmentcited by 1
- 2026EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprisescited by 1
- 2026A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedbackcited by 1
- 2025AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Modelscited by 0
- 2026A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback18 May 2026
- 2026From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences11 Apr 2026
- 2026EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises23 Mar 2026
- 2026Breaking Model Lock-in: Cost-Efficient Zero-Shot LLM Routing via a Universal Latent Space9 Jan 2026
- 2025AutoBench: Automating LLM Evaluation through Reciprocal Peer Assessment26 Oct 2025
- 2025SP-MoE: Speculative Decoding and Prefetching for Accelerating MoE-based Model Inference11 Oct 2025
- 2025AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models19 May 2025