Handling and retrieving information from various file types can be challenging. People often struggle with extracting content from PDFs and spreadsheets, especially when dealing with large volumes. This process can be time-consuming and inefficient, making it difficult to use the extracted information effectively for different applications, such as research or context augmentation. Existing solutions for… →

In Neural Networks, understanding how to optimize performance with a given computational budget is crucial. More processing power devoted to training neural networks usually results in better performance. However, choosing between expanding the training dataset and raising the model’s parameters is crucial when scaling computer resources. In order to optimize performance, these two factors must… →

Large language models (LLMs) have proven their potential to handle multiple tasks and perform extremely well across various applications. However, it is challenging for LLMs to generate accurate information, especially when the knowledge is less represented in their training data. To overcome this challenge, retrieval augmentation combines information retrieval and nearest neighbor search from a… →

The development and application of large language models (LLMs) have experienced significant advancements in Artificial Intelligence (AI). These models have demonstrated exceptional capabilities in understanding and generating human language, impacting various areas such as natural language processing, machine translation, and automated content creation. As these technologies continue to evolve, they promise to revolutionize how we… →

Scale AI has announced the launch of SEAL Leaderboards, an innovative and expert-driven ranking system for large language models (LLMs). This initiative is a product of the Safety, Evaluations, and Alignment Lab (SEAL) at Scale, which is dedicated to providing neutral, trustworthy evaluations of AI models. The SEAL Leaderboards aim to address the growing need… →

LLMs possess extraordinary natural language understanding capabilities, primarily derived from pretraining on extensive textual data. However, their adaptation to new or domain-specific knowledge is limited and can lead to inaccuracies. Knowledge Graphs (KGs) offer structured data storage, aiding in updates and facilitating tasks like Question Answering (QA). Retrieval-augmented generation (RAG) frameworks enhance LLM performance by… →

Natural Language Processing (NLP) has seen transformative advancements over the past few years, largely driven by the developing of sophisticated language models like transformers. Among these advancements, Retrieval-Augmented Generation (RAG) stands out as a cutting-edge technique that significantly enhances the capabilities of language models. RAG integrates retrieval mechanisms with generative models to create customizable, highly… →

Retrieval-augmented generation (RAG) is a potent strategy that improves the capabilities of Large Language Models (LLMs) by integrating outside knowledge.  However, RAG is prone to a particular type of attack known as retrieval corruption. In these types of attacks, malicious actors introduce destructive sections into the collection of retrieved documents, which leads to the model… →