no code implementations • 26 Feb 2024 • Ming Zhong, Yelong Shen, Shuohang Wang, Yadong Lu, Yizhu Jiao, Siru Ouyang, Donghan Yu, Jiawei Han, Weizhu Chen
Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images.
no code implementations • 11 Jan 2024 • Minhao Jiang, Ken Ziyu Liu, Ming Zhong, Rylan Schaeffer, Siru Ouyang, Jiawei Han, Sanmi Koyejo
Language models pre-trained on web-scale corpora demonstrate impressive capabilities on diverse downstream tasks.
1 code implementation • 16 Nov 2023 • Siru Ouyang, Zhuosheng Zhang, Bing Yan, Xuan Liu, Yejin Choi, Jiawei Han, Lianhui Qin
Large Language Models (LLMs) excel in diverse areas, yet struggle with complex scientific reasoning, especially in the field of chemistry.
1 code implementation • 24 Oct 2023 • Yizhu Jiao, Ming Zhong, Sha Li, Ruining Zhao, Siru Ouyang, Heng Ji, Jiawei Han
However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users.
1 code implementation • 19 Oct 2023 • Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks.
no code implementations • 11 Oct 2023 • Siru Ouyang, Jiaxin Huang, Pranav Pillai, Yunyi Zhang, Yu Zhang, Jiawei Han
In this study, we propose OnEFET, where we (1) enrich each node in the ontology structure with two types of extra information: instance information for training sample augmentation and topic information to relate types to contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples.
no code implementations • 4 Jul 2023 • Ming Zhong, Siru Ouyang, Minhao Jiang, Vivian Hu, Yizhu Jiao, Xuan Wang, Jiawei Han
Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design.
no code implementations • 13 Oct 2022 • Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang, Hai Zhao
In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base.
no code implementations • 29 Sep 2021 • Siru Ouyang, Zhuosheng Zhang, Hai Zhao
Pre-trained language models (PrLMs) have been shown useful for enhancing a broad range of natural language understanding (NLU) tasks.
1 code implementation • EMNLP 2021 • Zhuosheng Zhang, Siru Ouyang, Hai Zhao, Masao Utiyama, Eiichiro Sumita
In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference.
2 code implementations • NeurIPS 2021 • Siru Ouyang, Zhuosheng Zhang, Hai Zhao
Recent years have witnessed an increasing interest in training machines with reasoning ability, which deeply relies on accurately and clearly presented clue forms.
Ranked #23 on Reading Comprehension on ReClor
1 code implementation • Findings (ACL) 2021 • Siru Ouyang, Zhuosheng Zhang, Hai Zhao
Conversational Machine Reading (CMR) aims at answering questions in a complicated manner.