Search Results for author: Dhruv Kumar

Found 23 papers, 11 papers with code

mEdIT: Multilingual Text Editing via Instruction Tuning

no code implementations26 Feb 2024 Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bashar Alhafni, Dhruv Kumar

We introduce mEdIT, a multi-lingual extension to CoEdIT -- the recent state-of-the-art text editing models for writing assistance.

Grammatical Error Correction Text Simplification

Personalized Text Generation with Fine-Grained Linguistic Control

1 code implementation7 Feb 2024 Bashar Alhafni, Vivek Kulkarni, Dhruv Kumar, Vipul Raheja

As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized.

Text Generation

"Which LLM should I use?": Evaluating LLMs for tasks performed by Undergraduate Computer Science Students

no code implementations22 Jan 2024 Vibhor Agarwal, Madhav Krishan Garg, Sahiti Dharmavaram, Dhruv Kumar

This study evaluates the effectiveness of various large language models (LLMs) in performing tasks common among undergraduate computer science students.

Code Generation

Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools

no code implementations17 Dec 2023 Shreya Bhatia, Tarushi Gandhi, Dhruv Kumar, Pankaj Jalote

This research aims to experimentally investigate the effectiveness of LLMs, specifically exemplified by ChatGPT, for generating unit test scripts for Python programs, and how the generated test cases compare with those generated by an existing unit test generator (Pynguin).

Prompt Engineering

A Comparative Analysis of Large Language Models for Code Documentation Generation

no code implementations16 Dec 2023 Shubhang Shekhar Dvivedi, Vyshnav Vijay, Sai Leela Rahul Pujari, Shoumik Lodh, Dhruv Kumar

The paper evaluates models such as GPT-3. 5, GPT-4, Bard, Llama2, and Starchat on various parameters like Accuracy, Completeness, Relevance, Understandability, Readability and Time Taken for different levels of code documentation.

Code Documentation Generation

Can ChatGPT Play the Role of a Teaching Assistant in an Introductory Programming Course?

no code implementations12 Dec 2023 Anishka, Atharva Mehta, Nipun Gupta, Aarav Balachandran, Dhruv Kumar, Pankaj Jalote

The TA functions which we focus on include (1) grading student code submissions, and (2) providing feedback to undergraduate students in an introductory programming course.

"It's not like Jarvis, but it's pretty close!" -- Examining ChatGPT's Usage among Undergraduate Students in Computer Science

no code implementations16 Nov 2023 Ishika Joshi, Ritvik Budhiraja, Harshal D Akolekar, Jagat Sesh Challa, Dhruv Kumar

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

1 code implementation15 Nov 2023 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-answering.

Document Classification Question Answering

Speakerly: A Voice-based Writing Assistant for Text Composition

no code implementations24 Oct 2023 Dhruv Kumar, Vipul Raheja, Alice Kaiser-Schatzlein, Robyn Perry, Apurva Joshi, Justin Hugues-Nuger, Samuel Lou, Navid Chowdhury

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

no code implementations19 Sep 2023 Ishika Joshi, Ritvik Budhiraja, Pranav Deepak Tanna, Lovenya Jain, Mihika Deshpande, Arjun Srivastava, Srinivas Rallapalli, Harshal D Akolekar, Jagat Sesh Challa, Dhruv Kumar

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

1 code implementation17 May 2023 Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang

We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions).

Formality Style Transfer Grammatical Error Correction +5

Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks

1 code implementation2 Dec 2022 Zae Myung Kim, Wanyu Du, Vipul Raheja, Dhruv Kumar, Dongyeop Kang

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.

Grammatical Error Correction Sentence +3

Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision

1 code implementation In2Writing (ACL) 2022 Wanyu Du, Zae Myung Kim, Vipul Raheja, Dhruv Kumar, Dongyeop Kang

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.

LyricJam: A system for generating lyrics for live instrumental music

no code implementations3 Jun 2021 Olga Vechtomova, Gaurav Sahu, Dhruv Kumar

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.

Optimizing Deeper Transformers on Small Datasets

1 code implementation ACL 2021 Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J. D. Prince, Yanshuai Cao

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.

Reading Comprehension Semantic Parsing +2

Consentio: Managing Consent to Data Access using Permissioned Blockchains

1 code implementation16 Oct 2019 Rishav Raj Agarwal, Dhruv Kumar, Lukasz Golab, Srinivasan Keshav

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

1 code implementation WS 2019 Dhruv Kumar, Robin Cohen, Lukasz Golab

We propose an attention-based neural network approach to detect abusive speech in online social networks.

Abuse Detection

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