Search Results for author: Jing Gong

Found 9 papers, 4 papers with code

RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code Generation

1 code implementation21 Mar 2025 Linxi Liang, Jing Gong, Mingwei Liu, Chong Wang, Guangsheng Ou, Yanlin Wang, Xin Peng, Zibin Zheng

To address this gap, we present RustEvo, a novel framework for constructing dynamic benchmarks that evaluate the ability of LLMs to adapt to evolving Rust APIs.

Code Generation Navigate +1

Training Compute-Optimal Protein Language Models

1 code implementation4 Nov 2024 Xingyi Cheng, Bo Chen, Pan Li, Jing Gong, Jie Tang, Le Song

We explore optimally training protein language models, an area of significant interest in biological research where guidance on best practices is limited.

Language Modeling Language Modelling

A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning

no code implementations24 Sep 2024 Chenlin Wu, Xiaoyu He, Zike Li, Jing Gong, Zibin Zheng

In this work, we propose a non-isotropic sampling method to improve the gradient estimation procedure.

Federated Learning

CoSQA+: Pioneering the Multi-Choice Code Search Benchmark with Test-Driven Agents

1 code implementation17 Jun 2024 Jing Gong, Yanghui Wu, Linxi Liang, Yanlin Wang, Jiachi Chen, Mingwei Liu, Zibin Zheng

Semantic code search, retrieving code that matches a given natural language query, is an important task to improve productivity in software engineering.

Code Generation Code Search +4

ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inference

2 code implementations9 Sep 2022 Jing Gong, Hassaan Saadat, Hasindu Gamaarachchi, Haris Javaid, Xiaobo Sharon Hu, Sri Parameswaran

Compared to CPU-based approximate multiplier simulations in training and inference, the GPU-accelerated ApproxTrain is more than 2500x faster.

Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning

no code implementations6 Nov 2020 Yufan Jiang, Shuangzhi Wu, Jing Gong, Yahui Cheng, Peng Meng, Weiliang Lin, Zhibo Chen, Mu Li

In addition, by transferring knowledge from other kinds of MRC tasks, our model achieves a new state-of-the-art results in both single and ensemble settings.

AutoML Binary Classification +2

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