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of 10 755
pro vyhledávání: '"Parag, P"'
Autor:
Goswami, Ashish, Modi, Satyam Kumar, Deshineni, Santhosh Rishi, Singh, Harman, P, Prathosh A., Singla, Parag
Text-to-image (T2I) generation has seen significant progress with diffusion models, enabling generation of photo-realistic images from text prompts. Despite this progress, existing methods still face challenges in following complex text prompts, espe
Externí odkaz:
http://arxiv.org/abs/2412.06089
Autor:
Kumar, Shanu, Mendke, Saish, Rahman, Karody Lubna Abdul, Kurasa, Santosh, Agrawal, Parag, Dandapat, Sandipan
Chain-of-thought (CoT) prompting has significantly enhanced the capability of large language models (LLMs) by structuring their reasoning processes. However, existing methods face critical limitations: handcrafted demonstrations require extensive hum
Externí odkaz:
http://arxiv.org/abs/2412.00353
Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modelling objects and their evolving relationships over time. However, real-world visual relationships often exhibit a long-tailed distribution,
Externí odkaz:
http://arxiv.org/abs/2411.13059
Video geolocalization is a crucial problem in current times. Given just a video, ascertaining where it was captured from can have a plethora of advantages. The problem of worldwide geolocalization has been tackled before, but only using the image mod
Externí odkaz:
http://arxiv.org/abs/2411.06344
Autor:
Kumar, Shanu, Venkata, Akhila Yesantarao, Khandelwal, Shubhanshu, Santra, Bishal, Agrawal, Parag, Gupta, Manish
As large language models become increasingly central to solving complex tasks, the challenge of optimizing long, unstructured prompts has become critical. Existing optimization techniques often struggle to effectively handle such prompts, leading to
Externí odkaz:
http://arxiv.org/abs/2410.20788
One of today's most significant and transformative technologies is the rapidly developing field of artificial intelligence (AI). Deined as a computer system that simulates human cognitive processes, AI is present in many aspects of our daily lives, f
Externí odkaz:
http://arxiv.org/abs/2410.17139
The integration of machine learning into smart grid systems represents a transformative step in enhancing the efficiency, reliability, and sustainability of modern energy networks. By adding advanced data analytics, these systems can better manage th
Externí odkaz:
http://arxiv.org/abs/2410.15423
Autor:
Banerjee, Somnath, Layek, Sayan, Shrawgi, Hari, Mandal, Rajarshi, Halder, Avik, Kumar, Shanu, Basu, Sagnik, Agrawal, Parag, Hazra, Rima, Mukherjee, Animesh
As LLMs are increasingly deployed in global applications, the importance of cultural sensitivity becomes paramount, ensuring that users from diverse backgrounds feel respected and understood. Cultural harm can arise when these models fail to align wi
Externí odkaz:
http://arxiv.org/abs/2410.12880
Autor:
Wang, Yaxuan, Wei, Jiaheng, Liu, Chris Yuhao, Pang, Jinlong, Liu, Quan, Shah, Ankit Parag, Bao, Yujia, Liu, Yang, Wei, Wei
Unlearning in Large Language Models (LLMs) is essential for ensuring ethical and responsible AI use, especially in addressing privacy leak, bias, safety, and evolving regulations. Existing approaches to LLM unlearning often rely on retain data or a r
Externí odkaz:
http://arxiv.org/abs/2410.11143
Autor:
Pang, Jinlong, Wei, Jiaheng, Shah, Ankit Parag, Zhu, Zhaowei, Wang, Yaxuan, Qian, Chen, Liu, Yang, Bao, Yujia, Wei, Wei
Instruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outperform larger datasets, challenging traditional data scaling laws. Whi
Externí odkaz:
http://arxiv.org/abs/2410.10877