The need for speed and precision in today's digitally-fueled arena is ever-increasing, making it a challenge for search engines to meet these expectations. Traditional retrieval models present a trade-off between speed, accuracy, and computational cost. To address this, researchers from the University of Glasgow have offered a creative solution known as shallow Cross-Encoders. These small…
Large Language Models (LLMs) are widely used in complex reasoning tasks across various fields. But, their construction and optimization demand considerable computational power, particularly when pretraining on large datasets. To mitigate this, researchers have proposed scaling equations showing the relationship between pretraining loss and computational effort.
However, new findings suggest these rules may not thoroughly represent…
Natural Language Processing (NLP) has transformed with the advent of Transformer models. The document generation and summarization, machine translation, and speech recognition abilities of Transformers have exhibited significant progress. Their dominance is specifically seen in large language models (LLMs) that deal with more complex tasks through upscaling transformer architecture. However, the growth of the Transformer…
Stability AI, a leader in the AI sector, has announced the release of Stable Audio 2.0, an innovative model that enhances and introduces new features from its predecessor version. The model significantly augments creative possibilities for artists and musicians globally.
At the core of Stable Audio 2.0 is its unique ability to generate full-length tracks…
Artificial intelligence's progression in recent years has seen an increased focus on the development of multi-agent simulators. This technology aims to create virtual environments where AI agents can interact with their surroundings and each other, providing researchers with a unique opportunity to study social dynamics, collective behavior, and the development of complex systems. However, most…
The Dynamic Retrieval Augmented Generation (RAG) approach is designed to boost the performance of Large Language Models (LLMs) through determining when and what external information to retrieve during text generation. However, the current methods to decide when to recover data often rely on static rules and tend to limit retrieval to recent sentences or tokens,…
Researchers from Google DeepMind have introduced Gecko, a groundbreaking text embedding model to transform text into a form that machines can comprehend and act upon. Gecko is unique in its use of large language models (LLMs) for knowledge distillation. As opposed to conventional models that depend on comprehensive labeled datasets, Gecko initiates its learning journey…
Large language models (LLMs), such as those developed by Anthropic, OpenAI, and Google DeepMind, are vulnerable to a new exploit termed "many-shot jailbreaking," according to recent research by Anthropic. Through many-shot jailbreaking, the AI models can be manipulated by feeding them numerous question-answer pairs depicting harmful responses, thus bypassing the models' safety training.
This method manipulates…
In the modern digital era, information overload proves a significant challenge for both individuals and businesses. A multitude of files, emails, and notes often results in digital clutter, leading to increased difficulty in finding needed information and potentially hampering productivity. To combat this issue, Quivr has been developed as an open-source, robust AI assistant, aimed…
In today's data-driven world, managing copious amounts of information can be overwhelming and reduce productivity. Quivr, an open-source RAG framework and powerful AI assistant, seeks to alleviate this information overload issue faced by individuals and businesses. Unlike conventional tagging and folder methods, Quivr uses natural language processing to provide personalized search results within your files…