Zobrazeno 1 - 10
of 801
pro vyhledávání: '"Kumaraguru, P."'
Autor:
Joshi, Swarang, Mavani, Siddharth, Alex, Joel, Negi, Arnav, Mishra, Rahul, Kumaraguru, Ponnurangam
Misinformation undermines individual knowledge and affects broader societal narratives. Despite growing interest in the research community in multi-modal misinformation detection, existing methods exhibit limitations in capturing semantic cues, key r
Externí odkaz:
http://arxiv.org/abs/2410.15517
Autor:
Kumaru, Neha, Gupta, Garvit, Mongia, Shreyas, Singh, Shubham, Kumaraguru, Ponnurangam, Buduru, Arun Balaji
The use of Social media to share content is on a constant rise. One of the capsize effect of information sharing on Social media includes the spread of sensitive information on the public domain. With the digital gadget market becoming highly competi
Externí odkaz:
http://arxiv.org/abs/2409.04880
Autor:
Balappanawar, Ishwar B, Chamoli, Ashmit, Wickramarachchi, Ruwan, Mishra, Aditya, Kumaraguru, Ponnurangam, Sheth, Amit P.
Distracted driving is a leading cause of road accidents globally. Identification of distracted driving involves reliably detecting and classifying various forms of driver distraction (e.g., texting, eating, or using in-car devices) from in-vehicle ca
Externí odkaz:
http://arxiv.org/abs/2408.16621
Most existing Question Answering Datasets (QuADs) primarily focus on factoid-based short-context Question Answering (QA) in high-resource languages. However, the scope of such datasets for low-resource languages remains limited, with only a few works
Externí odkaz:
http://arxiv.org/abs/2408.10604
Autor:
Kavathekar, Ishan, Rani, Anku, Chamoli, Ashmit, Kumaraguru, Ponnurangam, Sheth, Amit, Das, Amitava
The widespread adoption of Large Language Models (LLMs) and awareness around multilingual LLMs have raised concerns regarding the potential risks and repercussions linked to the misapplication of AI-generated text, necessitating increased vigilance.
Externí odkaz:
http://arxiv.org/abs/2407.15694
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data, demonstrating remarkable performance across various tasks. Recognising their importance, there has been extensive research focused on e
Externí odkaz:
http://arxiv.org/abs/2406.03253
Autor:
Lee, Andrew H., Semnani, Sina J., Castillo-López, Galo, de Chalendar, Gäel, Choudhury, Monojit, Dua, Ashna, Kavitha, Kapil Rajesh, Kim, Sungkyun, Kodali, Prashant, Kumaraguru, Ponnurangam, Lombard, Alexis, Moradshahi, Mehrad, Park, Gihyun, Semmar, Nasredine, Seo, Jiwon, Shen, Tianhao, Shrivastava, Manish, Xiong, Deyi, Lam, Monica S.
Creating multilingual task-oriented dialogue (TOD) agents is challenging due to the high cost of training data acquisition. Following the research trend of improving training data efficiency, we show for the first time, that in-context learning is su
Externí odkaz:
http://arxiv.org/abs/2405.17840
Autor:
Kodali, Prashant, Goel, Anmol, Asapu, Likhith, Bonagiri, Vamshi Krishna, Govil, Anirudh, Choudhury, Monojit, Shrivastava, Manish, Kumaraguru, Ponnurangam
Current computational approaches for analysing or generating code-mixed sentences do not explicitly model "naturalness" or "acceptability" of code-mixed sentences, but rely on training corpora to reflect distribution of acceptable code-mixed sentence
Externí odkaz:
http://arxiv.org/abs/2405.05572
We investigate the impact of free speech and the relaxation of moderation on online social media platforms using Elon Musk's takeover of Twitter as a case study. By curating a dataset of over 10 million tweets, our study employs a novel framework com
Externí odkaz:
http://arxiv.org/abs/2404.11465
Proving geometric theorems constitutes a hallmark of visual reasoning combining both intuitive and logical skills. Therefore, automated theorem proving of Olympiad-level geometry problems is considered a notable milestone in human-level automated rea
Externí odkaz:
http://arxiv.org/abs/2404.06405