30 citations · 49 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…