Publications (13)
Train a Unified Multimodal Data Quality Classifier with Synthetic Data
Weizhi Wang, Rongmei Lin, Shiyang Li +7
The Multimodal Large Language Models (MLLMs) are continually pre-trained on a mixture of image-text caption data and interleaved document data, while the high-quality data filterin…
CoMem: Context Management with A Decoupled Long-Context Model
Yuwei Zhang, Chengyu Dong, Shuowei Jin +11
Context management enables agentic models to solve long-horizon tasks through iterative summarization of previous interaction histories. However, this process typically incurs subs…
CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web
Colin Lockard, Xin Luna Dong, Arash Einolghozati +1
The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DO…
ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages
Colin Lockard, Prashant Shiralkar, Xin Luna Dong +1
In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and col…
Label-Efficient Self-Training for Attribute Extraction from Semi-Structured Web Documents
Ritesh Sarkhel, Binxuan Huang, Colin Lockard +1
Extracting structured information from HTML documents is a long-studied problem with a broad range of applications, including knowledge base construction, faceted search, and perso…
TCN: Table Convolutional Network for Web Table Interpretation
Daheng Wang, Prashant Shiralkar, Colin Lockard +3
Information extraction from semi-structured webpages provides valuable long-tailed facts for augmenting knowledge graph. Relational Web tables are a critical component containing a…
DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities
Jing Yang Lee, Hamed Bonab, Nasser Zalmout +6
Large Language Models (LLMs) are increasingly employed in multi-turn conversational tasks, yet their pre-training data predominantly consists of continuous prose, creating a potent…
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference
Dongxu Zhang, Subhabrata Mukherjee, Colin Lockard +2
In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowled…
PLAtE: A Large-scale Dataset for List Page Web Extraction
Aidan San, Yuan Zhuang, Jan Bakus +6
Recently, neural models have been leveraged to significantly improve the performance of information extraction from semi-structured websites. However, a barrier for continued progr…
DOM-LM: Learning Generalizable Representations for HTML Documents
Xiang Deng, Prashant Shiralkar, Colin Lockard +2
HTML documents are an important medium for disseminating information on the Web for human consumption. An HTML document presents information in multiple text formats including unst…
Extracting Shopping Interest-Related Product Types from the Web
Yinghao Li, Colin Lockard, Prashant Shiralkar +1
Recommending a diversity of product types (PTs) is important for a good shopping experience when customers are looking for products around their high-level shopping interests (SIs)…
Semi-Supervised Event Extraction with Paraphrase Clusters
James Ferguson, Colin Lockard, Daniel S. Weld +1
Supervised event extraction systems are limited in their accuracy due to the lack of available training data. We present a method for self-training event extraction systems by boot…
ByteFlow: Language Modeling through Adaptive Byte Compression without a Tokenizer
Chunyuan Deng, Sanket Lokegaonkar, Colin Lockard +3
Modern language models still rely on fixed, pre-defined subword tokenizations. Once a tokenizer is trained, the LM can only operate at this fixed level of granularity, which often…