151 citations · 498 across the 65 of their papers we have counts for
18 papers · 1 filter
Learning Concise and Descriptive Attributes for Visual Recognition
An Yan, Yu Wang, Yiwu Zhong +6
Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attribute…
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
Zijie Huang, Daheng Wang, Binxuan Huang +8
Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact ma…
Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation
Letian Peng, Yuwei Zhang, Jingbo Shang
Prompting large language models (LLMs) for data augmentation has recently become a common practice in few-shot NLP tasks. In this paper, we propose Chain-of-Thought Attribute Manip…
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Hejie Cui, Rongmei Lin, Nasser Zalmout +4
Information extraction, e.g., attribute value extraction, has been extensively studied and formulated based only on text. However, many attributes can benefit from image-based extr…
Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach
Liyan Xu, Chenwei Zhang, Xian Li +2
We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention. Ou…
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Jiacheng Li, Ming Wang, Jin Li +4
Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequen…
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank
Dheeraj Mekala, Adithya Samavedhi, Chengyu Dong +1
Deep neural classifiers trained with cross-entropy loss (CE loss) often suffer from poor calibration, necessitating the task of out-of-distribution (OOD) detection. Traditional sup…
Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path
Zilong Wang, Jingbo Shang
The rapid growth of web pages and the increasing complexity of their structure poses a challenge for web mining models. Web mining models are required to understand the semi-struct…
A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches
Zihan Wang, Tianle Wang, Dheeraj Mekala +1
Etremely Weakly Supervised Text Classification (XWS-TC) refers to text classification based on minimal high-level human guidance, such as a few label-indicative seed words or class…
ClusterLLM: Large Language Models as a Guide for Text Clustering
Yuwei Zhang, Zihan Wang, Jingbo Shang
We introduce ClusterLLM, a novel text clustering framework that leverages feedback from an instruction-tuned large language model, such as ChatGPT. Compared with traditional unsupe…
Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation
Prashant Krishnan, Zilong Wang, Yangkun Wang +1
Recent advances of incorporating layout information, typically bounding box coordinates, into pre-trained language models have achieved significant performance in entity recognitio…
Debiasing Made State-of-the-art: Revisiting the Simple Seed-based Weak Supervision for Text Classification
Chengyu Dong, Zihan Wang, Jingbo Shang
Recent advances in weakly supervised text classification mostly focus on designing sophisticated methods to turn high-level human heuristics into quality pseudo-labels. In this pap…
Goal-Driven Explainable Clustering via Language Descriptions
Zihan Wang, Jingbo Shang, Ruiqi Zhong
Unsupervised clustering is widely used to explore large corpora, but existing formulations neither consider the users' goals nor explain clusters' meanings. We propose a new task f…
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting
William Hogan, Jiacheng Li, Jingbo Shang
Open-world Relation Extraction (OpenRE) has recently garnered significant attention. However, existing approaches tend to oversimplify the problem by assuming that all unlabeled te…
WOT-Class: Weakly Supervised Open-world Text Classification
Tianle Wang, Zihan Wang, Weitang Liu +1
State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes…
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space
Weitang Liu, Ying-Wai Li, Yi-Zhuang You +1
The output distribution of a neural network (NN) over the entire input space captures the complete input-output mapping relationship, offering insights toward a more comprehensive…
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification
Zi Lin, Jeremiah Liu, Jingbo Shang
Pre-trained seq2seq models excel at graph semantic parsing with rich annotated data, but generalize worse to out-of-distribution (OOD) and long-tail examples. In comparison, symbol…
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
Jiayun Zhang, Xiyuan Zhang, Xinyang Zhang +3
Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set…