Snowflake recently announced the launch of Arctic Embed L 2.0 and Arctic Embed M 2.0, two small and powerful embedding models tailored for multilingual search and retrieval. The Arctic Embed 2.0 models are available in two distinct variants: medium and large. Based on Alibaba’s GTE-multilingual framework, the medium model incorporates 305 million parameters, of which…
Language Agents (LAs) have recently become the focal point of research and development because of the significant advancement in large language models (LLMs). LLMs have demonstrated significant advancements in understanding and producing human-like text. LLMs perform various tasks with great performance and accuracy. Through well-designed prompts and carefully selected in-context demonstrations, LLM-based agents, such as…
Clear communication can be surprisingly difficult in today’s audio environments. Background noise, overlapping conversations, and the mix of audio and video signals often create challenges that disrupt clarity and understanding. These issues impact everything from personal calls to professional meetings and even content production. Despite improvements in audio technology, most existing solutions struggle to consistently…
Vision models are pivotal in enabling machines to interpret and analyze visual data. They are integral to tasks such as image classification, object detection, and segmentation, where raw pixel values from images are transformed into meaningful features through trainable layers. These systems, including convolutional neural networks (CNNs) and vision transformers, rely on efficient training processes…
Question answering (QA) emerged as a critical task in natural language processing, designed to generate precise answers to complex queries across diverse domains. Within this, medical QA poses unique challenges, focusing on the complex nature of healthcare information processing. Medical scenarios demand complex reasoning capabilities beyond simple information retrieval, as models must handle these scenarios…
Global-MMLU by researchers from Cohere For AI, EPFL, Hugging Face, Mila, McGill University & Canada CIFAR AI Chair, AI Singapore, National University of Singapore, Cohere, MIT, KAIST, Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, MIT, MIT-IBM Watson AI Lab, Carnegie Mellon University, CONICET & Universidad de Buenos Aires emerges as a transformative benchmark…
Integration of AI into clinical practices is very challenging, especially in radiology. While AI has proven to enhance the accuracy of diagnosis, its “black-box” nature often erodes clinicians’ confidence and acceptance. Current clinical decision support systems (CDSSs) are either not explainable or use methods like saliency maps and Shapley values, which do not give clinicians…
LLMs are driving major advances in research and development today. A significant shift has been observed in research objectives and methodologies toward an LLM-centric approach. However, they are associated with high expenses, making LLMs for large-scale utilization inaccessible to many. It is, therefore, a significant challenge to reduce the latency of operations, especially in dynamic…
The rapid adoption of Large Language Models (LLMs) in various industries calls for a robust framework to ensure their secure, ethical, and reliable deployment. Let’s look at 20 essential guardrails designed to uphold security, privacy, relevance, quality, and functionality in LLM applications. Security and Privacy Guardrails Inappropriate Content Filter: An essential safeguard against disseminating inappropriate…
The rapid advancement of AI technologies highlights the critical need for Large Language Models (LLMs) that can perform effectively across diverse linguistic and cultural contexts. A key challenge is the lack of evaluation benchmarks for non-English languages, which limits the potential of LLMs in underserved regions. Most existing evaluation frameworks are English-centric, creating barriers to…