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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)…

In advanced computing, the focus intensifies on creating more efficient data processing techniques. The modern world, increasingly reliant on data for decision-making, demands methods to swiftly and accurately interpret vast and complex datasets. This field’s significance spans diverse sectors, from healthcare to finance, where understanding data leads to insightful and impactful decisions. The crux of…

In 3D scene generation, a captivating challenge is the seamless integration of new objects into pre-existing 3D scenes. The ability to modify these complex digital environments is crucial, especially when aiming to enhance them with human-like creativity and intention. While adept at altering scene styles and appearances, earlier methods falter in inserting new objects consistently…

Navigating the intricate coding landscape often presents developers with a recurrent challenge – the disruptive back-and-forth between their code and external language models. This process involves a tedious dance of copying, pasting, and editing, leading to a fractured coding flow. While some developers have explored the use of ChatGPT during coding, the constant context-switching required…