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…
Artificial intelligence and deep learning models, despite their popularity and capacity, often struggle with generalization, particularly when they encounter data that differs from what they were trained on. This issue arises when the distribution of training and testing data varies, resulting in reduced model performance.
The concept of domain generalization has been introduced to combat…
Large Language Models (LLMs) have shown significant impact across various tasks within the software engineering space. Leveraging extensive open-source code datasets and Github-enabled models like CodeLlama, ChatGPT, and Codex, they can generate code and documentation, translate between programming languages, write unit tests, and identify and rectify bugs. AlphaCode is a pre-trained model that can help…
Artificial Intelligence (AI) is reshaping numerous aspects of life in the modern world, and the medical field is no exception. A remarkable breakthrough in this area has been achieved by OpenEvidence, a medical AI created under the Mayo Clinic Platform Accelerate. The AI has set a benchmark by scoring an impressive 90% on the United…
The ability of large Multimodal Language Models (MLLMs) to tackle visual math problems is currently the subject of intense interest. While MLLMs have performed remarkably well in visual scenarios, the extent to which they can fully understand and solve visual math problems remains unclear. To address these challenges, frameworks such as GeoQA and MathVista have…
The increased adoption and integration of large Language Models (LLMs) in the biomedical sector for interpretation, summary and decision-making support has led to the development of an innovative reliability assessment framework known as Reliability AssessMent for Biomedical LLM Assistants (RAmBLA). This research, led by Imperial College London and GSK.ai, puts a spotlight on the critical…