no code implementations • 11 Nov 2019 • Yunan Zhang, Xiang Cheng, Yufeng Zhang, Zihan Wang, Zhengqi Fang, Xiaoyan Wang, Zhenya Huang, ChengXiang Zhai
Answering complex questions involving multiple entities and relations is a challenging task.
no code implementations • 28 Nov 2019 • Yunan Zhang, Heting Gao, Tarek Abdelzaher
As the ongoing rapid urbanization takes place with an ever-increasing speed, fully modeling urban dynamics becomes more and more challenging, but also a necessity for socioeconomic development.
no code implementations • 4 Dec 2019 • Yunan Zhang, Xiang Cheng, Heting Gao, ChengXiang Zhai
We model the question answering on KG as a cooperative task between two agents, a knowledge graph reasoning agent and an information extraction agent.
no code implementations • 1 Jan 2021 • Yufeng Zhang, Yunan Zhang, ChengXiang Zhai
To classify images, neural networks extract features from raw inputs and then sum them up with fixed weights via the fully connected layer.
1 code implementation • 20 Dec 2021 • Dongfang Li, Baotian Hu, Qingcai Chen, Tujie Xu, Jingcong Tao, Yunan Zhang
Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification.
no code implementations • 28 Dec 2022 • Yunan Zhang, Le Yan, Zhen Qin, Honglei Zhuang, Jiaming Shen, Xuanhui Wang, Michael Bendersky, Marc Najork
We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation.
1 code implementation • 23 Feb 2023 • Yunan Zhang, Qingcai Chen
Named Entity Recognition (NER) models capable of Continual Learning (CL) are realistically valuable in areas where entity types continuously increase (e. g., personal assistants).
Continual Named Entity Recognition Knowledge Distillation +3
no code implementations • 3 Oct 2023 • Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, Jianfeng Gao
In this study, we introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs).
no code implementations • 22 Feb 2024 • Zhenning Zhang, Yunan Zhang, Suyu Ge, Guangwei Weng, Mridu Narang, Xia Song, Saurabh Tiwary
(2) An answer generation phase where the LLM populates the layouts with the retrieved content.
no code implementations • 22 Apr 2024 • Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudhary, Parul Chopra, Allie Del Giorno, Gustavo de Rosa, Matthew Dixon, Ronen Eldan, Dan Iter, Amit Garg, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Jamie Huynh, Mojan Javaheripi, Xin Jin, Piero Kauffmann, Nikos Karampatziakis, Dongwoo Kim, Mahoud Khademi, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Chen Liang, Weishung Liu, Eric Lin, Zeqi Lin, Piyush Madan, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Xia Song, Masahiro Tanaka, Xin Wang, Rachel Ward, Guanhua Wang, Philipp Witte, Michael Wyatt, Can Xu, Jiahang Xu, Sonali Yadav, Fan Yang, ZiYi Yang, Donghan Yu, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.