132 citations · 270 across the 24 of their papers we have counts for
6 papers · 1 filter
ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models
Jierui Li, Vipul Raheja, Dhruv Kumar
In recent times, large language models (LLMs) have shown impressive performance on various document-level tasks such as document classification, summarization, and question-answeri…
Speakerly: A Voice-based Writing Assistant for Text Composition
Dhruv Kumar, Vipul Raheja, Alice Kaiser-Schatzlein +5
We present Speakerly, a new real-time voice-based writing assistance system that helps users with text composition across various use cases such as emails, instant messages, and no…
Benchmarking Cognitive Biases in Large Language Models as Evaluators
Ryan Koo, Minhwa Lee, Vipul Raheja +3
Large Language Models are cognitively biased judges. Large Language Models (LLMs) have recently been shown to be effective as automatic evaluators with simple prompting and in-cont…
Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization
Oleksandr Yermilov, Vipul Raheja, Artem Chernodub
This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a var…
CoEdIT: Text Editing by Task-Specific Instruction Tuning
Vipul Raheja, Dhruv Kumar, Ryan Koo +1
We introduce CoEdIT, a state-of-the-art text editing system for writing assistance. CoEdIT takes instructions from the user specifying the attributes of the desired text, such as "…
Writing Assistants Should Model Social Factors of Language
Vivek Kulkarni, Vipul Raheja
Intelligent writing assistants powered by large language models (LLMs) are more popular today than ever before, but their further widespread adoption is precluded by sub-optimal pe…