no code implementations • 26 Dec 2023 • Junjie Wang, Yicheng Chen, Wangshu Zhang, Sen Hu, Teng Xu, Jing Zheng
In the second stage, we distill the knowledge from the existing teacher adapters into the student adapter to help its inference.
no code implementations • 2 Dec 2023 • Qiang Li, Xiaoyan Yang, Haowen Wang, Qin Wang, Lei Liu, Junjie Wang, Yang Zhang, Mingyuan Chu, Sen Hu, Yicheng Chen, Yue Shen, Cong Fan, Wangshu Zhang, Teng Xu, Jinjie Gu, Jing Zheng, Guannan Zhang Ant Group
(3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs.
1 code implementation • 6 Oct 2023 • Yinger Zhang, Hui Cai, Xeirui Song, Yicheng Chen, Rui Sun, Jing Zheng
While enabling large language models to implement function calling (known as APIs) can greatly enhance the performance of Large Language Models (LLMs), function calling is still a challenging task due to the complicated relations between different APIs, especially in a context-learning setting without fine-tuning.
no code implementations • 14 Jun 2023 • Sanat Sharma, Jayant Kumar, Jing Zheng, Tracy Holloway King
Adobe Fonts has a rich library of over 20, 000 unique fonts that Adobe users utilize for creating graphics, posters, composites etc.
1 code implementation • ACL 2021 • Xiang Hu, Haitao Mi, Zujie Wen, Yafang Wang, Yi Su, Jing Zheng, Gerard de Melo
Human language understanding operates at multiple levels of granularity (e. g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined.
no code implementations • 25 Apr 2021 • Wen Wang, Andreas Stolcke, Jing Zheng
In this paper, we investigate the use of linguistically motivated and computationally efficient structured language models for reranking N-best hypotheses in a statistical machine translation system.
no code implementations • 7 Mar 2021 • Huimin Huang, Ming Cai, Lanfen Lin, Jing Zheng, Xiongwei Mao, Xiaohan Qian, Zhiyi Peng, Jianying Zhou, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Ruofeng Tong
Our Graph- PGCR module is plug-and-play, which can be integrated into any architecture to improve its performance.
no code implementations • ICCV 2021 • Huimin Huang, Lanfen Lin, Yue Zhang, Yingying Xu, Jing Zheng, Xiongwei Mao, Xiaohan Qian, Zhiyi Peng, Jianying Zhou, Yen-Wei Chen, Ruofeng Tong
Semi-supervised learning (SSL) algorithms have attracted much attentions in medical image segmentation by leveraging unlabeled data, which challenge in acquiring massive pixel-wise annotated samples.