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While generating realistic tabular data, one of the difficulties faced by the researchers is maintaining privacy, especially in sensitive domains like finance and healthcare.  As the amount of data and the importance of data analysis is increasing in all fields and privacy concerns are leading to hesitancy in deploying AI models, the importance of maintaining…

Artificial intelligence (AI) systems are expanding and advancing at a significant pace. The two main categories into which AI systems have been divided are Predictive AI and Generative AI. The well-known Large Language Models (LLMs), which have recently gathered massive attention, are the best examples of generative AI. While Generative AI creates original content, Predictive…

With the growth of large language models, natural language processing has been revolutionized. Many LLMs, like GPT-3.5, LLaMA, and Mixtral, came up last year, which helped tackle diverse language tasks. Even though there are many such LLMs now, open-source models have no reliable models for translation tasks. Thorough research has been done to tackle this…

Developing large language models (LLMs) is a significant advancement in artificial intelligence and machine learning. Due to their vast size and complexity, these models have shown remarkable capabilities in understanding and generating human language. However, their extensive parameter count poses challenges regarding computational and memory resources, especially during the training phase. This has led to…

In the realm of conversational AI, the trend toward larger models, exemplified by behemoths like ChatGPT, Bard, and Gemini, has been palpable. The understanding is that increasing model parameters and training data significantly enhances language models’ quality and capabilities. However, the computational demands of these colossal models raise concerns about efficiency. When intelligently combined, can…

The field of natural language processing has been transformed by the advent of Large Language Models (LLMs), which provide a wide range of capabilities, from simple text generation to sophisticated problem-solving and conversational AI. Thanks to their sophisticated architectures and immense computational requirements, these models have become indispensable in cloud-based AI applications. However, deploying these…

In large language models (LLMs), the challenge of keeping information up-to-date is significant. As knowledge evolves, these models must adapt to include the latest information. However, updating LLMs traditionally involves retraining, which is resource-intensive. An alternative approach, model editing, offers a way to update the knowledge within these models more efficiently. This approach has garnered…

In human-computer interaction, the need to create ways for users to communicate with 3D environments has become increasingly important. This field of open-ended language queries in 3D has attracted researchers due to its various applications in robotic navigation and manipulation, 3D semantic understanding, and editing. However, current approaches have limitations of slow processing speeds and…

3D-aware Generative Adversarial Networks (GANs) have made remarkable advancements in generating multi-view-consistent images and 3D geometries from collections of 2D images through neural volume rendering. However, despite these advancements, a significant challenge has emerged due to the substantial memory and computational costs associated with dense sampling in volume rendering. This limitation has compelled 3D GANs…

The communication between the doctor and the patient is critical to providing effective and compassionate care. A medical interview is “the most powerful, sensitive, and versatile instrument available to the physician,” according to studies. It is thought that clinical history-taking accounts for 60-80% of diagnoses in certain contexts.  Advancements in general-purpose large language models (LLMs)…