36 citations · 96 across the 15 of their papers we have counts for
15 papers
Hybrid Forecasting of Geopolitical Events
Daniel M. Benjamin, Fred Morstatter, Ali E. Abbas +26
Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we deve…
WinoViz: Probing Visual Properties of Objects Under Different States
Woojeong Jin, Tejas Srinivasan, Jesse Thomason +1
Humans perceive and comprehend different visual properties of an object based on specific contexts. For instance, we know that a banana turns brown ``when it becomes rotten,'' wher…
GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions
Woojeong Jin, Subhabrata Mukherjee, Yu Cheng +5
Generalization to unseen tasks is an important ability for few-shot learners to achieve better zero-/few-shot performance on diverse tasks. However, such generalization to vision-l…
Analyzing Norm Violations in Live-Stream Chat
Jihyung Moon, Dong-Ho Lee, Hyundong Cho +6
Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and no…
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning
Dong-Ho Lee, Kian Ahrabian, Woojeong Jin +2
Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict future facts using knowledge of past facts. In this paper, we apply large language models (LLMs) t…
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer
Woojeong Jin, Dong-Ho Lee, Chenguang Zhu +2
Pre-trained language models are still far from human performance in tasks that need understanding of properties (e.g. appearance, measurable quantity) and affordances of everyday o…
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models
Woojeong Jin, Yu Cheng, Yelong Shen +2
Large pre-trained vision-language (VL) models can learn a new task with a handful of examples and generalize to a new task without fine-tuning. However, these VL models are hard to…
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding
Woojeong Jin, Maziar Sanjabi, Shaoliang Nie +3
To reduce a model size but retain performance, we often rely on knowledge distillation (KD) which transfers knowledge from a large "teacher" model to a smaller "student" model. How…
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
Woojeong Jin, Rahul Khanna, Suji Kim +4
Event forecasting is a challenging, yet important task, as humans seek to constantly plan for the future. Existing automated forecasting studies rely mostly on structured data, suc…
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
Sankalp Garg, Navodita Sharma, Woojeong Jin +1
Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowle…
Collaborative Policy Learning for Open Knowledge Graph Reasoning
Cong Fu, Tong Chen, Meng Qu +2
In recent years, there has been a surge of interests in interpretable graph reasoning methods. However, these models often suffer from limited performance when working on sparse an…
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
Woojeong Jin, Meng Qu, Xisen Jin +1
Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a ti…
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
Weizhi Ma, Min Zhang, Yue Cao +6
Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation…
Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees
Minji Yoon, Woojeong Jin, U Kang
Given a time-evolving graph, how can we track similarity between nodes in a fast and accurate way, with theoretical guarantees on the convergence and the error? Random Walk with Re…
Supervised and Extended Restart in Random Walks for Ranking and Link Prediction in Networks
Woojeong Jin, Jinhong Jung, U Kang
Given a real-world graph, how can we measure relevance scores for ranking and link prediction? Random walk with restart (RWR) provides an excellent measure for this and has been ap…