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20222024
most citedIndian Language Summarization using Pretrained Sequence-to-Sequence Models

7 citations · 13 across the 5 of their papers we have counts for

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cs.CL20241 cited

TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques

Ashok Urlana, Aditya Saibewar, Bala Mallikarjunarao Garlapati +3

The Large Language Models (LLMs) exhibit remarkable ability to generate fluent content across a wide spectrum of user queries. However, this capability has raised concerns regardin…

cs.CL2024

LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers

Abdur Rahman Bin Md Faizullah, Ashok Urlana, Rahul Mishra

Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids reade…

cs.CL2024

LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey

Ashok Urlana, Charaka Vinayak Kumar, Ajeet Kumar Singh +3

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks.…

cs.CL20235 cited

Assessing Translation capabilities of Large Language Models involving English and Indian Languages

Vandan Mujadia, Ashok Urlana, Yash Bhaskar +4

Generative Large Language Models (LLMs) have achieved remarkable advancements in various NLP tasks. In this work, our aim is to explore the multilingual capabilities of large langu…

cs.CL2023

Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey

Ashok Urlana, Pruthwik Mishra, Tathagato Roy +1

Generic text summarization approaches often fail to address the specific intent and needs of individual users. Recently, scholarly attention has turned to the development of summar…

cs.CL2023

PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India

Ashok Urlana, Pinzhen Chen, Zheng Zhao +3

This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India. Our corpus provides a training and testing ground for fo…