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…
K2 is a cutting-edge large language model (LLM) developed by LLM360 in collaboration with MBZUAI and Petuum. This model, known as K2-65B, boasts 65 billion parameters and is fully reproducible, meaning all artifacts, including code, data, model checkpoints, and intermediate results, are open-sourced and accessible to the public. This level of transparency aims to demystify…
Multimodal machine learning is a cutting-edge research field combining various data types, such as text, images, and audio, to create more comprehensive and accurate models. By integrating these different modalities, researchers aim to enhance the model’s ability to understand and reason about complex tasks. This integration allows models to leverage the strengths of each modality,…
The advent of deep neural networks (DNNs) has led to remarkable improvements in controlling artificial agents using the optimization of reinforcement learning or evolutionary algorithms. However, most neural networks show structural rigidity, binding their architectures to specific input and output space. This inflexibility is the major cause that prevents the optimization of neural networks across…
The use of Artificial Intelligence in sports is rapidly expanding, from post-game analysis and in-game activities to the fan experience. Here are some really cool AI tools in sports. Locks Using artificial intelligence algorithms, the Locks Player Props Research iOS app uncovers useful patterns and insights for sports betting. Users may make informed decisions using…
Scientists studying Large Language Models (LLMs) have found that LLMs perform similarly to humans in cognitive tasks, often making judgments and decisions that deviate from rational norms, such as risk and loss aversion. LLMs also exhibit human-like biases and errors, particularly in probability judgments and arithmetic operations tasks. These similarities suggest the potential for using…
Managing and extracting useful information from diverse and extensive documents is a significant challenge in data processing and artificial intelligence. Many organizations find it difficult to handle various file types and formats efficiently while ensuring the accuracy and relevance of the extracted data. This complexity often results in inefficiencies and errors, hindering productivity and decision-making…
The recent release of this open-source project, LlamaFS, addresses the challenges associated with traditional file management systems, particularly in the context of overstuffed download folders, inefficient file organization, and the limitations of knowledge-based organization. These issues arise due to the manual nature of file sorting, which often leads to inconsistent structures and difficulty finding specific…
Transformers are essential in modern machine learning, powering large language models, image processors, and reinforcement learning agents. Universal Transformers (UTs) are a promising alternative due to parameter sharing across layers, reintroducing RNN-like recurrence. UTs excel in compositional tasks, small-scale language modeling, and translation due to better compositional generalization. However, UTs face efficiency issues as parameter…