Search Results for author:
Found 17 papers, 9 papers with code
"It's not like Jarvis, but it's pretty close!" -- Examining ChatGPT's Usage among Undergraduate Students in Computer Science
However, our research also highlights various challenges that must be resolved for long-term acceptance of ChatGPT amongst students.
ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models
In recent times, large language models (LLMs) have shown impressive performance on various document-level tasks such as document classification, summarization, and question-answering.
Speakerly: A Voice-based Writing Assistant for Text Composition
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 notes.
"With Great Power Comes Great Responsibility!": Student and Instructor Perspectives on the influence of LLMs on Undergraduate Engineering Education
The rise in popularity of Large Language Models (LLMs) has prompted discussions in academic circles, with students exploring LLM-based tools for coursework inquiries and instructors exploring them for teaching and research.
CoEdIT: Text Editing by Task-Specific Instruction Tuning
We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions).
ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Computer Science Questions
ChatGPT is an AI language model developed by OpenAI that can understand and generate human-like text.
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Leveraging datasets from other related text editing NLP tasks, combined with the specification of editable spans, leads our system to more accurately model the process of iterative text refinement, as evidenced by empirical results and human evaluations.
Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision
Examining and evaluating the capability of large language models for making continuous revisions and collaborating with human writers is a critical step towards building effective writing assistants.
GRS: Combining Generation and Revision in Unsupervised Sentence Simplification
We propose GRS: an unsupervised approach to sentence simplification that combines text generation and text revision.
Understanding Iterative Revision from Human-Written Text
Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process.
LyricJam: A system for generating lyrics for live instrumental music
We describe a real-time system that receives a live audio stream from a jam session and generates lyric lines that are congruent with the live music being played.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Optimizing Deeper Transformers on Small Datasets
This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension.
Generation of lyrics lines conditioned on music audio clips
We present a system for generating novel lyrics lines conditioned on music audio.
Iterative Edit-Based Unsupervised Sentence Simplification
We present a novel iterative, edit-based approach to unsupervised sentence simplification.
Ranked #5 on
Text Simplification
on Newsela
Consentio: Managing Consent to Data Access using Permissioned Blockchains
The data management challenge we address is to ensure high throughput and low latency of endorsing data access requests and granting or revoking consent.
Distributed, Parallel, and Cluster Computing
Online abuse detection: the value of preprocessing and neural attention models
We propose an attention-based neural network approach to detect abusive speech in online social networks.
