no code implementations • 25 Apr 2024 • Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Chao Wang, Xinzhang Liu, Zihan Wang, Yu Zhao, Xin Wang, Yuyao Huang, Shuangyong Song, Yongxiang Li, Zheng Zhang, Bo Zhao, Aixin Sun, Yequan Wang, Zhongjiang He, Zhongyuan Wang, Xuelong Li, Tiejun Huang
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications.
no code implementations • 4 Mar 2024 • Siqi Fan, Xin Jiang, Xiang Li, Xuying Meng, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang, Zhongyuan Wang
To answer this question, we first indicate that Not all Layers are Necessary during Inference by statistically analyzing the activated layers across tasks.
no code implementations • 19 Feb 2024 • Xiaowei Yuan, Zhao Yang, Yequan Wang, Shengping Liu, Jun Zhao, Kang Liu
Large language models internalize enormous parametric knowledge during pre-training.
no code implementations • 4 Jan 2024 • Haitong Luo, Xuying Meng, Suhang Wang, Hanyun Cao, Weiyao Zhang, Yequan Wang, Yujun Zhang
In this study, we present a novel approach called Spectral-based Complementary Graph Neural Networks (SComGNN) that utilizes the spectral properties of complementary item graphs.
1 code implementation • 14 Dec 2023 • Xingrun Xing, Li Du, Xinyuan Wang, Xianlin Zeng, Yequan Wang, Zheng Zhang, Jiajun Zhang
Specifically, we first analyze the binarization error in self-attention operations and derive the polynomials of binarization error.
no code implementations • 11 Sep 2023 • Li Du, Yequan Wang, Xingrun Xing, Yiqun Ya, Xiang Li, Xin Jiang, Xuezhi Fang
Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs.
no code implementations • 7 Sep 2023 • Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Xuying Meng, Siqi Fan, Peng Han, Jing Li, Li Du, Bowen Qin, Zheng Zhang, Aixin Sun, Yequan Wang
We demonstrate that a 101B-parameter LLM with 0. 31T tokens can be trained with a budget of 100K US dollars.
no code implementations • 15 Jun 2023 • Jing Li, Yequan Wang, Shuai Zhang, Min Zhang
Recently, numerous efforts have continued to push up performance boundaries of document-level relation extraction (DocRE) and have claimed significant progress in DocRE.
1 code implementation • 4 May 2023 • Yiqun Yao, Zheng Zhang, Jing Li, Yequan Wang
In terms of growth schedule, the impact of each single dimension on a schedule's efficiency is under-explored by existing work.
no code implementations • 2 May 2023 • Xiang Li, Xin Jiang, Xuying Meng, Aixin Sun, Yequan Wang
FreeLM outperforms large models e. g., GPT-3 and InstructGPT, on a range of language understanding tasks in experiments.
no code implementations • 19 Apr 2023 • Xuying Meng, Chungang Lin, Yequan Wang, Yujun Zhang
Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks.
1 code implementation • 14 Apr 2023 • Yiqun Yao, Siqi Fan, Xiusheng Huang, Xuezhi Fang, Xiang Li, Ziyi Ni, Xin Jiang, Xuying Meng, Peng Han, Shuo Shang, Kang Liu, Aixin Sun, Yequan Wang
With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B.
no code implementations • 17 Mar 2023 • Jingxuan Wei, Shiyu Wu, Xin Jiang, Yequan Wang
We introduce DialogPaint, a novel framework that bridges conversational interactions with image editing, enabling users to modify images through natural dialogue.
no code implementations • 15 Mar 2023 • Yequan Wang, Hengran Zhang, Aixin Sun, Xuying Meng
Given comparative text, comparative relation extraction aims to extract two targets (\eg two cameras) in comparison and the aspect they are compared for (\eg image quality).
no code implementations • 23 Oct 2022 • Xiaohan Xu, Xuying Meng, Yequan Wang
Further experiments prove that abundant prior knowledge is conducive to high-quality emotional support, and a well-learned latent variable is critical to the diversity of generations.
no code implementations • 12 Oct 2022 • Yequan Wang, Jiawen Deng, Aixin Sun, Xuying Meng
Recently, amounts of works utilize perplexity~(PPL) to evaluate the quality of the generated text.
1 code implementation • COLING 2022 • Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, Jiafeng Guo
CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures.
no code implementations • 23 Mar 2022 • Yequan Wang, Xuying Meng, Yiyi Liu, Aixin Sun, Yao Wang, Yinhe Zheng, Minlie Huang
These models hence are not optimized for dialog-level emotion detection, i. e. to predict the emotion category of a dialog as a whole.
no code implementations • 5 Feb 2022 • Ting Lin, Aixin Sun, Yequan Wang
A sentence may express sentiments on multiple aspects.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
1 code implementation • Findings (NAACL) 2022 • Yiyi Liu, Yequan Wang, Aixin Sun, Xuying Meng, Jing Li, Jiafeng Guo
Based on this dual-channel framework, we design the Dual-Channel Network~(DC-Net) to recognize sentiment conflict.
no code implementations • 13 Feb 2020 • Funan Mu, Zhenting Yu, LiFeng Wang, Yequan Wang, Qingyu Yin, Yibo Sun, Liqun Liu, Teng Ma, Jing Tang, Xing Zhou
In addition, with the help of tokens, our model is able to extract overlapped keyphrases.
1 code implementation • The Web Conference (WWW) 2018 • Yequan Wang, Aixin Sun, Jialong Han, Ying Liu, Xiaoyan Zhu
Based on capsule representation, the probability module computes the capsule’s state probability.
Ranked #6 on Sentiment Analysis on MR