no code implementations • ACL 2022 • Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, Jie Tang
Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training.
1 code implementation • 3 Apr 2024 • Yifan Xu, Xiao Liu, Xinghan Liu, Zhenyu Hou, Yueyan Li, Xiaohan Zhang, Zihan Wang, Aohan Zeng, Zhengxiao Du, Wenyi Zhao, Jie Tang, Yuxiao Dong
Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving.
no code implementations • 1 Apr 2024 • Zhenyu Hou, Yilin Niu, Zhengxiao Du, Xiaohan Zhang, Xiao Liu, Aohan Zeng, Qinkai Zheng, Minlie Huang, Hongning Wang, Jie Tang, Yuxiao Dong
The work presents our practices of aligning LLMs with human preferences, offering insights into the challenges and solutions in RLHF implementations.
no code implementations • 23 Mar 2024 • Zhengxiao Du, Aohan Zeng, Yuxiao Dong, Jie Tang
Recent studies have put into question the belief that emergent abilities in language models are exclusive to large models.
1 code implementation • 15 Jan 2024 • Dan Zhang, Ziniu Hu, Sining Zhoubian, Zhengxiao Du, Kaiyu Yang, Zihan Wang, Yisong Yue, Yuxiao Dong, Jie Tang
To bridge these gaps, we introduce SciGLM, a suite of scientific language models able to conduct college-level scientific reasoning.
1 code implementation • 28 Aug 2023 • Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
In this paper, we introduce LongBench, the first bilingual, multi-task benchmark for long context understanding, enabling a more rigorous evaluation of long context understanding.
1 code implementation • 7 Aug 2023 • Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, Jie Tang
We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.
2 code implementations • 13 Jun 2023 • Xiao Liu, Hanyu Lai, Hao Yu, Yifan Xu, Aohan Zeng, Zhengxiao Du, Peng Zhang, Yuxiao Dong, Jie Tang
We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM).
10 code implementations • 5 Oct 2022 • Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, WenGuang Chen, Peng Zhang, Yuxiao Dong, Jie Tang
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on Language Modelling on CLUE (OCNLI_50K)
4 code implementations • 14 Oct 2021 • Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, Jie Tang
Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training.
9 code implementations • ACL 2022 • Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang
On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1. 25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks.
Ranked #4 on Language Modelling on WikiText-103 (using extra training data)
7 code implementations • 18 Mar 2021 • Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, Jie Tang
Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU).
1 code implementation • 19 Nov 2019 • Himank Yadav, Zhengxiao Du, Thorsten Joachims
is an abundant and attractive source of data for learning to rank, it can produce unfair ranking policies for both exogenous and endogenous reasons.
1 code implementation • 13 Jun 2019 • Zhengxiao Du, Chang Zhou, Ming Ding, Hongxia Yang, Jie Tang
Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently.
1 code implementation • 2 Jun 2019 • Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, Jie Tang
Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks.