Retrieval-augmented generation (RAG) systems are essential in enhancing language model performance by integrating external knowledge sources into their workflows. These systems utilize methods that divide documents into smaller, manageable sections called chunks. RAG systems aim to improve both the accuracy and contextual relevance of their outputs by retrieving contextually appropriate chunks and feeding them into… →
While today’s LLMs can skillfully use various tools, they still operate synchronously, only processing one action at a time. This strict turn-based setup limits their ability to handle multiple tasks simultaneously, reducing interactivity and responsiveness. For example, in a hypothetical scenario with an AI travel assistant, the model can’t respond to a quick weather query… →
The exponential growth of multi-dimensional data across various fields, such as machine learning, geospatial analysis, and clustering, has posed significant challenges to traditional data structures. One such structure, the kd-tree, has long been a fundamental tool for managing high-dimensional datasets, supporting queries like nearest neighbors, range searches, and clustering analysis. However, the rapidly increasing size… →
Large language models (LLMs) have revolutionized natural language processing by offering sophisticated abilities for a range of applications. However, these models face significant challenges. First, deploying these massive models on end devices, such as smartphones or personal computers, is extremely resource-intensive, making integration impractical for everyday applications. Second, current LLMs are monolithic, storing all domain… →
Agentic AI has emerged as a result of the quick development of Artificial Intelligence (AI). This new wave of AI is changing industries and reinventing how humans and machines work together. It is distinguished by its autonomous decision-making and problem-solving capabilities. In contrast to conventional generative AI, which concentrates on producing content, agentic AI enables… →
Marqo has introduced four groundbreaking datasets and state-of-the-art e-commerce embedding models designed to advance product search, retrieval, and recommendation capabilities in e-commerce. These models, Marqo-Ecommerce-B and Marqo-Ecommerce-L, offer substantial improvements in accuracy and relevance for e-commerce platforms by delivering high-quality embedding representations of product data. Alongside these models, Marqo has released a series of evaluation… →
Despite their advanced reasoning capabilities, the latest LLMs often miss the mark when deciphering relationships. In this article, we explore the Reversal Curse, a pitfall that affects LLMs across tasks such as comprehension and generation. To understand the underlying issue, it is a phenomenon that occurs when dealing with two entities, denoted as a and… →
In real-world settings, agents often face limited visibility of the environment, complicating decision-making. For instance, a car-driving agent must recall road signs from moments earlier to adjust its speed, yet storing all observations is unscalable due to memory limits. Instead, agents must learn compressed representations of observations. This challenge is compounded in ongoing tasks, where… →
Edge AI has long faced the challenge of balancing efficiency and effectiveness. Deploying Vision Language Models (VLMs) on edge devices is difficult due to their large size, high computational demands, and latency issues. Models designed for cloud environments often struggle with the limited resources of edge devices, resulting in excessive battery usage, slower response times,… →
Advancements in large language models (LLMs) have revolutionized natural language processing, with applications spanning text generation, translation, and summarization. These models rely on large amounts of data, large parameter counts, and expansive vocabularies, necessitating sophisticated techniques to manage computational and memory requirements. A critical component of LLM training is the cross-entropy loss computation, which, while… →