Search Results for author: Vipul Raheja

Found 19 papers, 12 papers with code

Spivavtor: An Instruction Tuned Ukrainian Text Editing Model

no code implementations29 Apr 2024 Aman Saini, Artem Chernodub, Vipul Raheja, Vivek Kulkarni

We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language.

Grammatical Error Correction Text Simplification

mEdIT: Multilingual Text Editing via Instruction Tuning

1 code implementation26 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

Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs

1 code implementation16 Feb 2024 Zae Myung Kim, Kwang Hee Lee, Preston Zhu, Vipul Raheja, Dongyeop Kang

With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred.

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

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.

Benchmarking Cognitive Biases in Large Language Models as Evaluators

1 code implementation29 Sep 2023 Ryan Koo, Minhwa Lee, Vipul Raheja, Jong Inn Park, Zae Myung Kim, Dongyeop Kang

According to our findings, LLMs may still be unable to be utilized for automatic annotation aligned with human preferences.

Benchmarking In-Context Learning

Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization

no code implementations8 Jun 2023 Oleksandr Yermilov, Vipul Raheja, Artem Chernodub

Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques to better balance the trade-offs between data protection and utility preservation.

text-classification Text Classification

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

Writing Assistants Should Model Social Factors of Language

no code implementations28 Mar 2023 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 performance.

Position

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

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

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.

Text Simplification by Tagging

1 code implementation EACL (BEA) 2021 Kostiantyn Omelianchuk, Vipul Raheja, Oleksandr Skurzhanskyi

Edit-based approaches have recently shown promising results on multiple monolingual sequence transduction tasks.

 Ranked #1 on Text Simplification on PWKP / WikiSmall (SARI metric)

Text Simplification

Adversarial Grammatical Error Correction

no code implementations Findings of the Association for Computational Linguistics 2020 Vipul Raheja, Dimitrios Alikaniotis

The discriminator is a sentence-pair classification model, trained to judge a given pair of grammatically incorrect-correct sentences on the quality of grammatical correction.

Grammatical Error Correction Machine Translation +4

The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction

2 code implementations WS 2019 Dimitrios Alikaniotis, Vipul Raheja

Recent work on Grammatical Error Correction (GEC) has highlighted the importance of language modeling in that it is certainly possible to achieve good performance by comparing the probabilities of the proposed edits.

Grammatical Error Correction Language Modelling

Dialogue Act Classification with Context-Aware Self-Attention

1 code implementation NAACL 2019 Vipul Raheja, Joel Tetreault

Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks.

Classification Dialogue Act Classification +2

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