Zobrazeno 1 - 10
of 741
pro vyhledávání: '"SAHA, SRIPARNA"'
We often summarize a multi-party conversation in two stages: chunking with homogeneous units and summarizing the chunks. Thus, we hypothesize that there exists a correlation between homogeneous speaker chunking and overall summarization tasks. In thi
Externí odkaz:
http://arxiv.org/abs/2407.15237
Despite recent advancements in federated learning (FL) for medical image diagnosis, addressing data heterogeneity among clients remains a significant challenge for practical implementation. A primary hurdle in FL arises from the non-IID nature of dat
Externí odkaz:
http://arxiv.org/abs/2407.05800
Autor:
Jha, Prince, Jain, Raghav, Mandal, Konika, Chadha, Aman, Saha, Sriparna, Bhattacharyya, Pushpak
In the digital world, memes present a unique challenge for content moderation due to their potential to spread harmful content. Although detection methods have improved, proactive solutions such as intervention are still limited, with current researc
Externí odkaz:
http://arxiv.org/abs/2406.05344
In an era of rapidly evolving internet technology, the surge in multimodal content, including videos, has expanded the horizons of online communication. However, the detection of toxic content in this diverse landscape, particularly in low-resource c
Externí odkaz:
http://arxiv.org/abs/2405.20628
The mining of adverse drug events (ADEs) is pivotal in pharmacovigilance, enhancing patient safety by identifying potential risks associated with medications, facilitating early detection of adverse events, and guiding regulatory decision-making. Tra
Externí odkaz:
http://arxiv.org/abs/2405.15766
With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this w
Externí odkaz:
http://arxiv.org/abs/2405.11181
The rapid advancement of foundation models (FMs) across language, image, audio, and video domains has shown remarkable capabilities in diverse tasks. However, the proliferation of FMs brings forth a critical challenge: the potential to generate hallu
Externí odkaz:
http://arxiv.org/abs/2405.09589
The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primarily adept at processing textual information. To address this constrain
Externí odkaz:
http://arxiv.org/abs/2404.07214
Autor:
Sahoo, Pranab, Singh, Ayush Kumar, Saha, Sriparna, Jain, Vinija, Mondal, Samrat, Chadha, Aman
Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known as prompts, to enhance model effi
Externí odkaz:
http://arxiv.org/abs/2402.07927
Over the past few years, the use of the Internet for healthcare-related tasks has grown by leaps and bounds, posing a challenge in effectively managing and processing information to ensure its efficient utilization. During moments of emotional turmoi
Externí odkaz:
http://arxiv.org/abs/2401.05134