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In large language models, understanding how they work and what they pay attention to is crucial for improving their performance. However, analyzing the attention patterns of these models, especially in large-scale scenarios, can be daunting. Researchers and developers often need to gain insights into how tokens interact with each other during processing. Existing solutions for…
Researchers from C4DM, Queen Mary University of London, Sony AI, and Music X Lab, MBZUAI, have introduced Instruct-MusicGen to address the challenge of text-to-music editing, where textual queries are used to modify music, such as changing its style or adjusting instrumental components. Current methods are required to train specific models from scratch, are resource-intensive, and…
In a groundbreaking development, Timescale, the PostgreSQL cloud database company, has introduced two revolutionary open-source extensions, pgvectorscale, and pgai. These innovations have made PostgreSQL faster than Pinecone for AI workloads and 75% cheaper. Let’s explore how these extensions work and their implications for AI application development. Introduction to pgvectorscale and pgai Timescale unveiled the pgvectorscale…
Most LMMs integrate vision and language by converting images into visual tokens fed as sequences into LLMs. While effective for multimodal understanding, this method significantly increases memory and computation demands, especially with high-resolution photos or videos. Various techniques, like spatial grouping and token compression, aim to reduce the number of visual tokens but often compromise…
Large language models (LLMs) have achieved remarkable success across various domains, but training them centrally requires massive data collection and annotation efforts, making it costly for individual parties. Federated learning (FL) has emerged as a promising solution, enabling collaborative training of LLMs on decentralized data while preserving privacy (FedLLM). Although frameworks like OpenFedLLM, FederatedScope-LLM, and…
Human-computer interaction (HCI) focuses on designing and using computer technology, particularly the interfaces between people (users) and computers. Researchers in this field observe how humans interact with computers & design technologies that let humans interact with computers in novel ways. HCI encompasses various areas, such as user experience design, ergonomics, and cognitive psychology, aiming to…
Retrieval Augmented Generation (RAG) is a method that enhances the capabilities of Large Language Models (LLMs) by integrating a document retrieval system. This integration allows LLMs to fetch relevant information from external sources, thereby improving the accuracy and relevance of the responses generated. This approach addresses the limitations of traditional LLMs, such as the need…
Large language models (LLMs) have the potential to lead users to make poor decisions, especially when these models provide incorrect information with high confidence, which is called hallucination. This confident misinformation has the potential to be very dangerous since it might persuade people to act based on erroneous assumptions, which could have negative consequences. A…
The training of Large Language Models (LLMs) like GPT-3 and Llama on a large scale faces significant inefficiencies due to hardware failures and network congestion. These issues lead to substantial GPU resource waste and extended training durations. Specifically, hardware malfunctions cause interruptions in training, and network congestions force GPUs to wait for parameter synchronization, further…
Apple made a significant announcement, strongly advocating for on-device AI through its newly introduced Apple Intelligence. This innovative approach emphasizes the integration of a ~3 billion parameter language model (LLM) on devices like Mac, iPhone, and iPad, leveraging fine-tuned LoRA adapters to perform specialized tasks. This model claims to outperform larger models, such as the…