Despite the advancements in LLMs, the current models still need to continually improve to incorporate new knowledge without losing previously acquired information, a problem known as catastrophic forgetting. Current methods, such as retrieval-augmented generation (RAG), have limitations in performing tasks that require integrating new knowledge across different passages since it encodes passages in isolation, making…
The crucial challenge of enhancing logical reasoning capabilities in Large Language Models (LLMs) is pivotal for achieving human-like reasoning, a fundamental step towards realizing Artificial General Intelligence (AGI). Current LLMs exhibit impressive performance in various natural language tasks but often need more logical reasoning, limiting their applicability in scenarios requiring deep understanding and structured problem-solving.…
Ordered sequences, including text, audio, and code, rely on position information for meaning. Large language models (LLMs), like the Transformer architecture, lack inherent ordering information and treat sequences as sets. Position Encoding (PE) addresses this by assigning an embedding vector to each position, which is crucial for LLMs’ understanding. PE methods, including absolute and relative…
IBM plays a crucial role in advancing AI by developing cutting-edge technologies and offering comprehensive courses. Through its AI initiatives, IBM empowers learners to harness the potential of AI in various fields. Its courses provide practical skills and knowledge, enabling individuals to implement AI solutions effectively and drive innovation in their respective domains. This article…
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…