Search Results for author: Yuchi Ma

Found 8 papers, 3 papers with code

RepoTransBench: A Real-World Benchmark for Repository-Level Code Translation

no code implementations23 Dec 2024 Yanli Wang, Yanlin Wang, Suiquan Wang, Daya Guo, Jiachi Chen, John Grundy, Xilin Liu, Yuchi Ma, Mingzhi Mao, Hongyu Zhang, Zibin Zheng

However, even with this improvement, the Success@1 score of the best-performing LLM is only 21%, which may not meet the need for reliable automatic repository-level code translation.

Code Translation Translation

Agents in Software Engineering: Survey, Landscape, and Vision

1 code implementation13 Sep 2024 Yanlin Wang, Wanjun Zhong, Yanxian Huang, Ensheng Shi, Min Yang, Jiachi Chen, Hui Li, Yuchi Ma, Qianxiang Wang, Zibin Zheng

In recent years, Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks, especially in the tasks of the software engineering (SE) field.

Survey

Exploring and Evaluating Hallucinations in LLM-Powered Code Generation

no code implementations1 Apr 2024 Fang Liu, Yang Liu, Lin Shi, Houkun Huang, Ruifeng Wang, Zhen Yang, Li Zhang, Zhongqi Li, Yuchi Ma

The rise of Large Language Models (LLMs) has significantly advanced many applications on software engineering tasks, particularly in code generation.

Code Generation Hallucination +3

Learning county from pixels: Corn yield prediction with attention-weighted multiple instance learning

no code implementations2 Dec 2023 Xiaoyu Wang, Yuchi Ma, Qunying Huang, Zhengwei Yang, Zhou Zhang

Furthermore, through an in-depth study of the relationship between mixed pixels and attention, it is verified that our approach can capture critical feature information while filtering out noise from mixed pixels.

Multiple Instance Learning

SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution

no code implementations30 Sep 2023 Gengchen Mai, Ni Lao, Weiwei Sun, Yuchi Ma, Jiaming Song, Chenlin Meng, Hongxu Ma, Jinmeng Rao, Ziyuan Li, Stefano Ermon

Existing digital sensors capture images at fixed spatial and spectral resolutions (e. g., RGB, multispectral, and hyperspectral images), and each combination requires bespoke machine learning models.

Spectral Super-Resolution Super-Resolution

PanGu-Coder: Program Synthesis with Function-Level Language Modeling

1 code implementation22 Jul 2022 Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, Guchun Zhang, Yinpeng Guo, Zhongqi Li, Qi Zhang, Meng Xiao, Bo Shen, Lin Li, Hao Yu, Li Yan, Pingyi Zhou, Xin Wang, Yuchi Ma, Ignacio Iacobacci, Yasheng Wang, Guangtai Liang, Jiansheng Wei, Xin Jiang, Qianxiang Wang, Qun Liu

We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i. e. the synthesis of programming language solutions given a natural language problem description.

Code Generation Decoder +4

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