252 citations · 554 across the 32 of their papers we have counts for
21 papers · 1 filter
When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning
Jiaqi Wei, Xuehang Guo, Pengfei Yu +5
In single-stream autoregressive interfaces, the same tokens both update the model state and constitute an irreversible public commitment. This coupling creates a silence tax: addit…
Anagent For Enhancing Scientific Table & Figure Analysis
Xuehang Guo, Zhiyong Lu, Tom Hope +1
In scientific research, analysis requires accurately interpreting complex multimodal knowledge, integrating evidence from different sources, and drawing inferences grounded in doma…
Atomic Reasoning for Scientific Table Claim Verification
Yuji Zhang, Qingyun Wang, Cheng Qian +7
Scientific texts often convey authority due to their technical language and complex data. However, this complexity can sometimes lead to the spread of misinformation. Non-experts a…
Explain Less, Understand More: Data-Efficient Personalization of Reader-Dependent Jargons
Bohao Wu, Qingyun Wang, Yue Guo
Personalizing jargon detection and explanation is essential for making technical documents accessible to readers with diverse disciplinary backgrounds. However, tailoring models to…
MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness
Junsheng Huang, Zhitao He, Yucheng Huang +3
The hallucination of non-existent facts by LLMs is an important problem given its widespread adoption across various applications. Previous research addresses this problem by analy…
CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering
Yumeng Wang, Zhiyuan Fan, Qingyun Wang +2
Large Language Models (LLMs) are pretrained on extensive multilingual corpora to acquire both language-specific cultural knowledge and general knowledge. Ideally, while LLMs should…
MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders
Cheng Li, May Fung, Qingyun Wang +4
Mental health disorders are one of the most serious diseases in the world. Most people with such a disease lack access to adequate care, which highlights the importance of training…
Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks
Jiayi He, Hehai Lin, Qingyun Wang +2
While Vision-Language Models (VLMs) have shown remarkable abilities in visual and language reasoning tasks, they invariably generate flawed responses. Self-correction that instruct…
Automating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Framework
Yuting Hu, Dancheng Liu, Qingyun Wang +5
Identifying effective interventions from the scientific literature is challenging due to the high volume of publications, specialized terminology, and inconsistent reporting format…
L+M-24: Building a Dataset for Language + Molecules @ ACL 2024
Carl Edwards, Qingyun Wang, Lawrence Zhao +1
Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molec…
Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences
Hongyi Liu, Qingyun Wang, Payam Karisani +1
Named entity recognition is a key component of Information Extraction (IE), particularly in scientific domains such as biomedicine and chemistry, where large language models (LLMs)…
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction
Qingyun Wang, Zixuan Zhang, Hongxiang Li +4
Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges. First, compared with entity extraction tasks in the general domain, sentences from chemi…
SciMON: Scientific Inspiration Machines Optimized for Novelty
Qingyun Wang, Doug Downey, Heng Ji +1
We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generation has trad…
Multimedia Generative Script Learning for Task Planning
Qingyun Wang, Manling Li, Hou Pong Chan +4
Goal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotyp…
Stage-wise Fine-tuning for Graph-to-Text Generation
Qingyun Wang, Semih Yavuz, Victoria Lin +2
Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders. However, they fail to fully utilize t…
ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
Qingyun Wang, Qi Zeng, Lifu Huang +3
To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful compar…
A Survey of Knowledge-Enhanced Text Generation
Wenhao Yu, Chenguang Zhu, Zaitang Li +4
The goal of text generation is to make machines express in human language. It is one of the most important yet challenging tasks in natural language processing (NLP). Since 2014, v…
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang, Manling Li, Xuan Wang +24
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and rel…
PaperRobot: Incremental Draft Generation of Scientific Ideas
Qingyun Wang, Lifu Huang, Zhiying Jiang +4
We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and co…
Describing a Knowledge Base
Qingyun Wang, Xiaoman Pan, Lifu Huang +4
We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can…
Paper Abstract Writing through Editing Mechanism
Qingyun Wang, Zhihao Zhou, Lifu Huang +4
We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract. We design…