Retrieval-augmented generation (RAG) is a cutting-edge technique in artificial intelligence that combines the strengths of retrieval-based approaches with generative models. This integration allows for creating high-quality, contextually relevant responses by leveraging vast datasets. RAG has significantly improved the performance of virtual assistants, chatbots, and information retrieval systems by ensuring that generated responses are accurate and…
The increasing availability of digital text in diverse languages and scripts presents a significant challenge for natural language processing (NLP). Multilingual pre-trained language models (mPLMs) often struggle to handle transliterated data effectively, leading to performance degradation. Addressing this issue is crucial for improving cross-lingual transfer learning and ensuring accurate NLP applications across various languages and…
Video understanding is one of the evolving areas of research in artificial intelligence (AI), focusing on enabling machines to comprehend and analyze visual content. Tasks like recognizing objects, understanding human actions, and interpreting events within a video come under this domain. Advancements in this domain find crucial applications in autonomous driving, surveillance, and entertainment industries.…
Large Language Models (LLMs) such as ChatGPT have attracted a lot of attention since they can perform a wide range of activities, including language processing, knowledge extraction, reasoning, planning, coding, and tool use. These abilities have sparked research into creating even more sophisticated AI models and hint at the possibility of Artificial General Intelligence (AGI). …
Transformer-based neural networks have shown great ability to handle multiple tasks like text generation, editing, and question-answering. In many cases, models that use more parameters show better performance measured by perplexity and high accuracies of end tasks. This is the main reason for the development of larger models in industries. However, larger models sometimes result…
Google AI researchers describe their novel approach to addressing the challenge of generating high-quality synthetic datasets that preserve user privacy, which are essential for training predictive models without compromising sensitive information. As machine learning models increasingly rely on large datasets, ensuring the privacy of individuals whose data contributes to these models becomes crucial. Differentially private…
Autonomous robotics has seen significant advancements over the years, driven by the need for robots to perform complex tasks in dynamic environments. At the heart of these advancements lies the development of robust planning architectures that enable robots to plan, perceive, and execute tasks autonomously. Let’s delve into the various planning architectures for autonomous robotics,…
Incorporating demonstrating examples, known as in-context learning (ICL), significantly enhances large language models (LLMs) and large multimodal models (LMMs) without requiring parameter updates. Recent studies confirm the efficacy of few-shot multimodal ICL, particularly in improving LMM performance on out-of-domain tasks. With longer context windows in advanced models like GPT-4o and Gemini 1.5 Pro, researchers can…
Machine learning models, which can contain billions of parameters, require sophisticated methods to fine-tune their performance efficiently. Researchers aim to enhance the accuracy of these models while minimizing the computational resources needed. This improvement is crucial for practical applications in various domains, such as natural language processing & artificial intelligence, where efficient resource utilization can…
Accurate propagation modeling is paramount for effective radio deployments, coverage analysis, and interference mitigation in wireless communications. Path loss modeling, a widely adopted approach, enables generic predictions of signal power attenuation along wireless links, equipping network planners with essential insights into physical layer attributes. However, in non-line-of-sight (NLOS) scenarios, traditional models like Longley-Rice and free…