In an industry where large corporations like OpenAI, Meta, and Google dominate, Paris-based AI startup Mistral has recently launched its open-source language model, Mixtral 8x22B. This bold venture establishes Mistral as a notable contender in the field of AI, while simultaneously challenging established models with its commitment to open-source development.
Mixtral 8x22B impressively features an advanced…
In the continuously evolving realm of AI frameworks, two significantly recognized entities known as LlamaIndex and LangChain have come to the forefront. Both of them provide exclusive approaches to boost the performance and capabilities of large language models (LLMs), but address the varying needs and preferences of the developer community. This comparison discusses their key…
Large Language Models (LLMs), outstanding in language understanding and reasoning tasks, still lack expertise in the crucial field of spatial reasoning exploration, an area where human cognition shines. Humans are capable of powerful mental imagery, coined as the Mind's Eye, enabling them to imagine the unseen world, a concept largely untouched in the realm of…
A group of researchers have created a novel assessment system, CodeEditorBench, designed to evaluate the effectiveness of Large Language Models (LLMs) in various code editing tasks such as debugging, translating, and polishing. LLMs, which have greatly advanced due to the rise of coding-related jobs, are mainly used for programming activities such as code improvement and…
Researchers at the University of Texas at Austin and Rembrand have developed a new language model known as VOICECRAFT. This Nvidia's technology uses textless natural language processing (NLP), marking a significant milestone in the field as it aims to make NLP tasks applicable directly to spoken utterances.
VOICECRAFT is a transformative, neural codec language model (NCLM)…
Researchers from the University of Waterloo, Carnegie Mellon University, and the Vector Institute in Toronto have made significant strides in the development of Large Language Models (LLMs). Their research has been focused on improving the models' capabilities to process and understand long contextual sequences for complex classification tasks.
The team has introduced LongICLBench, a benchmark developed…
Traditional training methods for Large Language Models (LLMs) have been limited by the constraints of subword tokenization, a process that requires significant computational resources and hence drives up costs. These limitations result in a ceiling on scalability and a restriction on working with large datasets. Accountability for these challenges with subword tokenization lies in finding…
Large Language Models (LLMs) have gained immense technological prowess over the recent years, thanks largely to the exponential growth of data on the internet and ongoing advancements in pre-training methods. Despite their progress, LLMs' dependency on English datasets limits their performance in other languages. This challenge, known as the "curse of multilingualism," suggests that models…
Developers and data scientists who use Large Language Models (LLMs) such as GPT-4 to leverage their AI capabilities often need tools to help navigate the complex processes involved. A selection of these crucial tools are highlighted here.
Hugging Face extends beyond its AI platform to offer a comprehensive ecosystem for hosting AI models, sharing datasets,…
The impressive advancements that have been seen in artificial intelligence, specifically in Large Language Models (LLMs), have seen them become a vital tool in many applications. However, the high cost associated with the computational power needed to train these models has limited their accessibility, stifling wider development. There have been several open-source resources attempting to…
Visually rich documents (VRDs) such as invoices, utility bills, and insurance quotes present unique challenges in terms of information extraction (IE). The varied layouts and formats, coupled with both textual and visual properties, require complex, resource-intensive solutions. Many existing strategies rely on supervised learning, which necessitates a vast pool of human-labeled training samples. This not…