7 citations · 8 across the 5 of their papers we have counts for
5 papers
Enhancing Incremental Summarization with Structured Representations
EunJeong Hwang, Yichao Zhou, James Bradley Wendt +4
Large language models (LLMs) often struggle with processing extensive input contexts, which can lead to redundant, inaccurate, or incoherent summaries. Recent methods have used uns…
STRUM-LLM: Attributed and Structured Contrastive Summarization
Beliz Gunel, James B. Wendt, Jing Xie +4
Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages. We propose STRUM-LLM that addresse…
An Augmentation Strategy for Visually Rich Documents
Jing Xie, James B. Wendt, Yichao Zhou +2
Many business workflows require extracting important fields from form-like documents (e.g. bank statements, bills of lading, purchase orders, etc.). Recent techniques for automatin…
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models
Yichao Zhou, James B. Wendt, Navneet Potti +2
A key bottleneck in building automatic extraction models for visually rich documents like invoices is the cost of acquiring the several thousand high-quality labeled documents that…
Active Learning for Skewed Data Sets
Abbas Kazerouni, Qi Zhao, Jing Xie +2
Consider a sequential active learning problem where, at each round, an agent selects a batch of unlabeled data points, queries their labels and updates a binary classifier. While t…