Previous research on Retrieval-Augmented Generation (RAG) in large language models (LLMs) concentrated on enhancing retrieval models to improve document selection for generation tasks. Initial studies established the benefits of integrating external information into LLMs, but recent extensions to noisy environments often focused on a limited range of noise types, typically assuming noise negatively impacted model…
A significant challenge in the field of artificial intelligence, particularly in generative modeling, is understanding how diffusion models can effectively learn and generate high-dimensional data distributions. Despite their empirical success, the theoretical mechanisms that enable diffusion models to avoid the curse of dimensionality—where the number of required samples increases exponentially with data dimension—remain poorly understood.…
The intersection of computational physics and machine learning has brought significant progress in understanding complex systems, particularly through neural networks. Graph neural networks (GNNs) have emerged as powerful tools for modeling interactions within physical systems, capitalizing on their ability to manage data-rich environments. Recently, there has been a shift toward directly incorporating domain-specific knowledge, such…
A significant bottleneck in large language models (LLMs) that hampers their deployment in real-world applications is the slow inference speeds. LLMs, while powerful, require substantial computational resources to generate outputs, leading to delays that can negatively impact user experience, increase operational costs, and limit the practical use of these models in time-sensitive scenarios. As LLMs…
Speculative decoding is emerging as a vital strategy to enhance high-throughput long-context inference, especially as the need for inference with large language models (LLMs) continues to grow across numerous applications. Together AI’s research on speculative decoding tackles the problem of improving inference throughput for LLMs that deal with long input sequences and large batch sizes.…
In computer vision, backbone architectures are critical in image recognition, object detection, and semantic segmentation tasks. These backbones extract local and global features from images, enabling machines to understand complex patterns. Traditionally, convolutional layers have been the primary component in these models, but recent advancements incorporate attention mechanisms, which enhance the model’s ability to capture…
Online social networks have become essential to modern communication, shaping how individuals share information, express opinions, and engage. Platforms like Reddit facilitate large-scale discussions, enabling millions of users to participate in conversations about various topics. One area of interest for researchers is understanding how these online conversations evolve when users are asked to make moral…
Building on LG AI Research’s remarkable achievements in AI language models, especially with the launch of EXAONE 3.0, the development of EXAONEPath represents another significant milestone. This new chapter in EXAONE’s journey focuses on transforming digital histopathology, a critical area in medical diagnostics, by addressing the complex challenges of Whole Slide Images (WSIs) in histopathology…
Medical LLMs like ClinicalCamel 70B and Llama3-OpenBioLLM 70B have shown strong performance in various medical NLP tasks, but no model specifically tailored to the cancer domain currently exists. Additionally, these models, with billions of parameters, are computationally demanding for many healthcare systems. A cancer-focused LLM, integrating specialized cancer knowledge, could significantly improve diagnosis and treatment…
Apple’s latest release, the iPhone 16, places a strong emphasis on on-device artificial intelligence (AI), powered by its new Apple Intelligence platform. Unlike cloud-reliant AI systems, Apple is focusing on maintaining privacy by processing AI functions directly on the device with the A18 Bionic chip. This enables faster, more personalized, and more secure interactions, marking…