1 code implementation • 26 Apr 2024 • Zhengwei Tao, Zhi Jin, Yifan Zhang, Xiancai Chen, Xiaoying Bai, Yue Fang, Haiyan Zhao, Jia Li, Chongyang Tao
Based on these findings, we introduce two methods to guide the LLMs to utilize the event schema knowledge.
no code implementations • 18 Apr 2024 • Zhengwei Tao, Xiancai Chen, Zhi Jin, Xiaoying Bai, Haiyan Zhao, Yiwei Lou
We conduct extensive experiments on event reasoning tasks on several datasets.
1 code implementation • 16 Apr 2024 • Zhengwei Tao, Zhi Jin, Junqiang Huang, Xiancai Chen, Xiaoying Bai, Haiyan Zhao, Yifan Zhang, Chongyang Tao
Finally, we observe that models trained in this way are still struggling to fully comprehend event evolution.
1 code implementation • 10 Apr 2024 • Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
In this paper, we explore the hypothesis that LLMs process concepts of varying complexities in different layers, introducing the idea of "Concept Depth" to suggest that more complex concepts are typically acquired in deeper layers.
1 code implementation • 13 Mar 2024 • Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, Mengnan Du, Ninghao Liu
Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI.
no code implementations • 16 Feb 2024 • Haiyan Zhao, Fan Yang, Bo Shen, Himabindu Lakkaraju, Mengnan Du
Large language models (LLMs) have led to breakthroughs in language tasks, yet the internal mechanisms that enable their remarkable generalization and reasoning abilities remain opaque.
no code implementations • 11 Jan 2024 • Chengfeng Dou, Zhi Jin, Wenpin Jiao, Haiyan Zhao, Yongqiang Zhao, Zhenwei Tao
The use of large language models in medical dialogue generation has garnered significant attention, with a focus on improving response quality and fluency.
1 code implementation • 10 Jan 2024 • Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du
Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models.
no code implementations • 31 Oct 2023 • Yongqiang Zhao, Zhenyu Li, Zhi Jin, Feng Zhang, Haiyan Zhao, Chengfeng Dou, Zhengwei Tao, Xinhai Xu, Donghong Liu
The Multi-Modal Large Language Model (MLLM) refers to an extension of the Large Language Model (LLM) equipped with the capability to receive and infer multi-modal data.
no code implementations • 17 Sep 2023 • Zirui He, Huiqi Deng, Haiyan Zhao, Ninghao Liu, Mengnan Du
Recent research has shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks.
Natural Language Understanding Out-of-Distribution Generalization
no code implementations • 2 Sep 2023 • Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Mengnan Du
For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge.
no code implementations • 10 Jul 2023 • Haiyan Zhao, Guodong Long
Large-scale pre-trained models have been remarkably successful in resolving downstream tasks.
no code implementations • 24 May 2023 • Zhengwei Tao, Zhi Jin, Xiaoying Bai, Haiyan Zhao, Yanlin Feng, Jia Li, Wenpeng Hu
In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects.
no code implementations • 19 May 2023 • Chengfeng Dou, Zhi Jin, Wenping Jiao, Haiyan Zhao, Zhenwei Tao, Yongqiang Zhao
PlugMed is equipped with two modules, the prompt generation (PG) module and the response ranking (RR) module, to enhances LLMs' dialogue strategies for improving the specificity of the dialogue.
no code implementations • 9 Apr 2023 • Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
In this paper, we study which modules in neural networks are more prone to forgetting by investigating their training dynamics during CL.
no code implementations • 27 Jan 2023 • Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
To address these challenges, we create a small model for a new task from the pruned models of similar tasks.
no code implementations • 29 Sep 2021 • Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Liming Zhu, Chengqi Zhang
Can we find a better initialization for a new task, e. g., a much smaller network closer to the final pruned model, by exploiting its similar tasks?
no code implementations • 5 Feb 2021 • Wenjie Chu, Wei zhang, Haiyan Zhao, Zhi Jin, Hong Mei
Self-assembly plays an essential role in many natural processes, involving the formation and evolution of living or non-living structures, and shows potential applications in many emerging domains.
Multiagent Systems Distributed, Parallel, and Cluster Computing Robotics
no code implementations • 1 Jan 2021 • Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
In this paper, we introduce an efficient method, \name, to extract the local inference chains by optimizing a differentiable sparse scoring for the filters and layers to preserve the outputs on given data from a local region.
no code implementations • 28 Nov 2018 • Bo Shen, Wei zhang, Haiyan Zhao, Zhi Jin, Yanhong Wu
And through feedback, each player is provided with personalized feedback information based on the current COG and the player's exploration result, in order to accelerate his/her puzzle-solving process.