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

1 code implementation • 21 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 implementation • 4 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 implementations • 24 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 implementation • 17 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

xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

no code implementations • 11 Jan 2024 • Bo Chen, Xingyi Cheng, Pan Li, Yangli-ao Geng, Jing Gong, Shen Li, Zhilei Bei, Xu Tan, Boyan Wang, Xin Zeng, Chiming Liu, Aohan Zeng, Yuxiao Dong, Jie Tang, Le Song

We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework.

Language Modeling Protein Language Model

xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data

no code implementations • NeurIPS 2023 • Jing Gong, Minsheng Hao, Xingyi Cheng, Xin Zeng, Chiming Liu, Jianzhu Ma, Xuegong Zhang, Taifeng Wang, Le Song

Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level.

Decoder Representation Learning

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

2 code implementations • 9 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 implementations • 6 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

Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters

no code implementations • 20 Oct 2020 • Shaohuai Shi, Xianhao Zhou, Shutao Song, Xingyao Wang, Zilin Zhu, Xue Huang, Xinan Jiang, Feihu Zhou, Zhenyu Guo, Liqiang Xie, Rui Lan, Xianbin Ouyang, Yan Zhang, Jieqian Wei, Jing Gong, Weiliang Lin, Ping Gao, Peng Meng, Xiaomin Xu, Chenyang Guo, Bo Yang, Zhibo Chen, Yongjian Wu, Xiaowen Chu

Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters.