Zobrazeno 1 - 10
of 280
pro vyhledávání: '"Kumaraguru, Ponnurangam"'
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
Recent advancements in graph learning contributed to explaining predictions generated by Graph Neural Networks. However, existing methodologies often fall short when applied to real-world datasets. We introduce HOGE, a framework to capture higher-ord
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
Autor:
Li, Nathaniel, Pan, Alexander, Gopal, Anjali, Yue, Summer, Berrios, Daniel, Gatti, Alice, Li, Justin D., Dombrowski, Ann-Kathrin, Goel, Shashwat, Phan, Long, Mukobi, Gabriel, Helm-Burger, Nathan, Lababidi, Rassin, Justen, Lennart, Liu, Andrew B., Chen, Michael, Barrass, Isabelle, Zhang, Oliver, Zhu, Xiaoyuan, Tamirisa, Rishub, Bharathi, Bhrugu, Khoja, Adam, Zhao, Zhenqi, Herbert-Voss, Ariel, Breuer, Cort B., Marks, Samuel, Patel, Oam, Zou, Andy, Mazeika, Mantas, Wang, Zifan, Oswal, Palash, Lin, Weiran, Hunt, Adam A., Tienken-Harder, Justin, Shih, Kevin Y., Talley, Kemper, Guan, John, Kaplan, Russell, Steneker, Ian, Campbell, David, Jokubaitis, Brad, Levinson, Alex, Wang, Jean, Qian, William, Karmakar, Kallol Krishna, Basart, Steven, Fitz, Stephen, Levine, Mindy, Kumaraguru, Ponnurangam, Tupakula, Uday, Varadharajan, Vijay, Wang, Ruoyu, Shoshitaishvili, Yan, Ba, Jimmy, Esvelt, Kevin M., Wang, Alexandr, Hendrycks, Dan
The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government ins
Externí odkaz:
http://arxiv.org/abs/2403.03218
Autor:
Govil, Priyanshul, Jain, Hemang, Bonagiri, Vamshi Krishna, Chadha, Aman, Kumaraguru, Ponnurangam, Gaur, Manas, Dey, Sanorita
Large Language Models (LLMs) are trained on extensive web corpora, which enable them to understand and generate human-like text. However, this training process also results in inherent biases within the models. These biases arise from web data's dive
Externí odkaz:
http://arxiv.org/abs/2402.14889