Language Foundation Models (LFMs) and Large Language Models (LLMs) have demonstrated their ability to handle multiple tasks efficiently with a single fixed model. This achievement has motivated the development of Image Foundation Models (IFMs) in computer vision, which aim to encode general information from images into embedding vectors. However, using these techniques poses a challenge…
Retrieval Augmented Generation (RAG) represents a cutting-edge advancement in Artificial Intelligence, particularly in NLP and Information Retrieval (IR). This technique is designed to enhance the capabilities of Large Language Models (LLMs) by seamlessly integrating contextually relevant, timely, and domain-specific information into their responses. This integration allows LLMs to perform more accurately and effectively in knowledge-intensive…
The Mixture of Experts (MoE) models enhance performance and computational efficiency by selectively activating subsets of model parameters. While traditional MoE models utilize homogeneous experts with identical capacities, this approach limits specialization and parameter utilization, especially when handling varied input complexities. Recent studies highlight that homogeneous experts tend to converge to similar representations, reducing their…
As Large Language Models (LLMs) become increasingly prevalent in long-context applications like interactive chatbots and document analysis, serving these models with low latency and high throughput has emerged as a significant challenge. Conventional wisdom suggests that techniques like speculative decoding (SD), while effective for reducing latency, are limited in improving throughput, especially for larger batch…
The release of DocChat by Cerebras marks a major milestone in document-based conversational question-answering systems. Cerebras, known for its deep expertise in machine learning (ML) and large language models (LLMs), has introduced two new models under the DocChat series: Cerebras Llama3-DocChat and Cerebras Dragon-DocChat. These models are designed to deliver high-performance conversational AI, specifically tailored…
The field of large language models (LLMs) has rapidly evolved, particularly in specialized domains like medicine, where accuracy and reliability are crucial. In healthcare, these models promise to significantly enhance diagnostic accuracy, treatment planning, and the allocation of medical resources. However, the challenges inherent in managing the system state and avoiding errors within these models…
Artificial intelligence (AI) development, particularly in large language models (LLMs), focuses on aligning these models with human preferences to enhance their effectiveness and safety. This alignment is critical in refining AI interactions with users, ensuring that the responses generated are accurate and aligned with human expectations and values. Achieving this requires a combination of preference…
Understanding spoken language for large language models (LLMs) is crucial for creating more natural and intuitive interactions with machines. While traditional models excel at text-based tasks, they struggle with comprehending human speech, limiting their potential in real-world applications like voice assistants, customer service, and accessibility tools. Enhancing speech understanding can improve interactions between humans and…
Large Language Models (LLMs) like GPT-4, Qwen2, and LLaMA have revolutionized artificial intelligence, particularly in natural language processing. These Transformer-based models, trained on vast datasets, have shown remarkable capabilities in understanding and generating human language, impacting healthcare, finance, and education sectors. However, LLMs need more domain-specific knowledge, real-time information, and proprietary data outside their training…
Repeatedly switching back and forth between various AI tools and applications to perform simple tasks like grammar checks or content edits can be daunting. This constant back-and-forth often wastes time and interrupts workflow, which hinders the efficiency of the process. Users usually find themselves juggling multiple tabs and copying and pasting text, complicating straightforward tasks.…