1 code implementation • 21 Mar 2024 • Qiushi Sun, Zhirui Chen, Fangzhi Xu, Kanzhi Cheng, Chang Ma, Zhangyue Yin, Jianing Wang, Chengcheng Han, Renyu Zhu, Shuai Yuan, Qipeng Guo, Xipeng Qiu, Pengcheng Yin, XiaoLi Li, Fei Yuan, Lingpeng Kong, Xiang Li, Zhiyong Wu
Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence, uncovering new cross-domain opportunities and illustrating the substantial influence of code intelligence across various domains.
1 code implementation • 24 Feb 2024 • Haiteng Zhao, Chang Ma, Guoyin Wang, Jing Su, Lingpeng Kong, Jingjing Xu, Zhi-Hong Deng, Hongxia Yang
Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behavior.
no code implementations • 5 Feb 2024 • Fei Yuan, Chang Ma, Shuai Yuan, Qiushi Sun, Lei LI
We further theoretically prove that KS-Lottery can find the certified winning tickets in the embedding layer, fine-tuning on the found parameters is guaranteed to perform as well as full fine-tuning.
2 code implementations • 24 Jan 2024 • Chang Ma, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin, Zhenzhong Lan, Lingpeng Kong, Junxian He
Evaluating large language models (LLMs) as general-purpose agents is essential for understanding their capabilities and facilitating their integration into practical applications.
1 code implementation • NeurIPS 2023 • Haiteng Zhao, Shengchao Liu, Chang Ma, Hannan Xu, Jie Fu, Zhi-Hong Deng, Lingpeng Kong, Qi Liu
We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks.
1 code implementation • 7 Mar 2023 • Yudong Wang, Chang Ma, Qingxiu Dong, Lingpeng Kong, Jingjing Xu
Experiments on a wide range of models show that neural networks, even pre-trained language models, have sharp performance drops on our benchmark, demonstrating the effectiveness on evaluating the weaknesses of neural networks.
1 code implementation • 24 Feb 2023 • Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Lingpeng Kong
RSA links query protein sequences to a set of sequences with similar structures or properties in the database and combines these sequences for downstream prediction.
1 code implementation • 5 Jun 2022 • Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang
However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.
1 code implementation • 24 May 2022 • Haiteng Zhao, Chang Ma, Xinshuai Dong, Anh Tuan Luu, Zhi-Hong Deng, Hanwang Zhang
Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples.
1 code implementation • 16 Feb 2022 • Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, Chang Ma, Runcheng Liu, Louis-Pascal Xhonneux, Meng Qu, Jian Tang
However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.
1 code implementation • 23 Oct 2021 • Haiteng Zhao, Chang Ma, Qinyu Chen, Zhi-Hong Deng
In the framework, a surrogate joint distribution models the underlying joint distribution of the unlabeled target domain.