238 citations · 451 across the 13 of their papers we have counts for
13 papers
Bot or Human? Detecting ChatGPT Imposters with A Single Question
Hong Wang, Xuan Luo, Weizhi Wang +1
Large language models (LLMs) like GPT-4 have recently demonstrated impressive capabilities in natural language understanding and generation. However, there is a concern that they c…
Explanations from Large Language Models Make Small Reasoners Better
Shiyang Li, Jianshu Chen, Yelong Shen +9
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In thi…
Limitations of Language Models in Arithmetic and Symbolic Induction
Jing Qian, Hong Wang, Zekun Li +2
Recent work has shown that large pretrained Language Models (LMs) can not only perform remarkably well on a range of Natural Language Processing (NLP) tasks but also start improvin…
Lifelong Learning of Hate Speech Classification on Social Media
Jing Qian, Hong Wang, Mai ElSherief +1
Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. However, the amount of data in social media increases every…
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering
Wenhan Xiong, Hong Wang, William Yang Wang
To extract answers from a large corpus, open-domain question answering (QA) systems usually rely on information retrieval (IR) techniques to narrow the search space. Standard inver…
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
Wenhu Chen, Hanwen Zha, Zhiyu Chen +3
Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distribu…
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering
Wenhan Xiong, Mo Yu, Xiaoxiao Guo +4
A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain…
Fine-tune Bert for DocRED with Two-step Process
Hong Wang, Christfried Focke, Rob Sylvester +2
Modelling relations between multiple entities has attracted increasing attention recently, and a new dataset called DocRED has been collected in order to accelerate the research on…
TabFact: A Large-scale Dataset for Table-based Fact Verification
Wenhu Chen, Hongmin Wang, Jianshu Chen +5
The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language u…
Meta Reasoning over Knowledge Graphs
Hong Wang, Wenhan Xiong, Mo Yu +3
The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations. While most existing st…
TWEETQA: A Social Media Focused Question Answering Dataset
Wenhan Xiong, Jiawei Wu, Hong Wang +5
With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective…
Self-Supervised Learning for Contextualized Extractive Summarization
Hong Wang, Xin Wang, Wenhan Xiong +4
Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.…
Sentence Embedding Alignment for Lifelong Relation Extraction
Hong Wang, Wenhan Xiong, Mo Yu +3
Conventional approaches to relation extraction usually require a fixed set of pre-defined relations. Such requirement is hard to meet in many real applications, especially when new…