Managing multiple AI agents in a system can often be a daunting task due to the need for effective communication, reliable task execution, and optimal scalability. Many of the available frameworks for managing multi-agent systems often lack in features such as flexibility, usability, and scalability. They also often require extensive manual setup, making it challenging…
Advancements in Large Language Models (LLMs) have significantly improved the field of information extraction (IE), a task in Natural Language Processing (NLP) that involves identifying and extracting specific information from text. LLMs demonstrate impressive results in IE, particularly when combined with instruction tuning, training the models to annotate text according to predefined standards, enhancing their…
Introducing Claude 3.5 Sonnet by Anthropic AI–an advanced large language model (LLM) that impresses with remarkable capabilities, far exceeding its predecessors. This development in artificial intelligence transcends previously identified boundaries, paving the way for numerous new applications.
Claude 3.5 Sonnet is exceptional in multiple ways. Firstly, it can efficiently generate complex n-body particle animations quickly and…
Researchers from Stony Brook University, the US Naval Academy, and the University of Texas at Austin have developed CAT-BENCH, a benchmark to assess language models' ability to predict the sequence of steps in cooking recipes. The research's main focus was on how language models comprehend plans by examining their understanding of the temporal sequencing of…
Two AI, a new startup in the artificial intelligence (AI) space, has launched SUTRA, an innovative language model capable of proficiency in over 30 languages. It includes many South Asian languages such as Gujarati, Marathi, Tamil, and Telugu, aiming to address the unique linguistic challenges and opportunities in South Asia.
Constructed by using two mixture-of-experts transformers…
Large language models (LLMs), instrumental in natural language processing tasks like translation, summarization, and text generation, face challenges in consistently adhering to logical constraints during text generation. This adherence is crucial in sensitive applications where precision and instruction compliance are crucial. Traditional methods for imposing constraints on LLMs, such as the GeLaTo framework, have limitations…
Large language models (LLMs) are central to the field of natural language processing, being utilized in tasks like translation, summarization, and creative text generation. They utilize extensive data to learn patterns and relationships in languages, enabling them to undertake tasks necessitating an understanding of context, syntax, and semantics. However, there's a persistent challenge in ensuring…
The paper discusses the challenge of ensuring that large language models (LLMs) generate accurate, credible, and verifiable responses. This is difficult as the current methods often require assistance due to errors and hallucinations, which results in incorrect or misleading information. To address this, the researchers introduce a new verification framework to improve the accuracy and…
Large Language Models (LLMs), which have immense computational needs, have revolutionized a variety of artificial intelligence (AI) applications, yet the efficient delivery of multiple LLMs remains a challenge due to their computational requirements. Present methods, like spatial partitioning that designates different GPU groups for each LLM, need improvement as lack of concurrency leads to resource…
The intersecting potential of AI systems and high-performance computing (HPC) platforms is becoming increasingly apparent in the scientific research landscape. AI models like ChatGPT, developed on the basis of transformer architecture and with the ability to train on extensive amounts of internet-scale data, have laid the groundwork for significant scientific breakthroughs. These include black hole…
Artificial Intelligence (AI) has demonstrated transformative potential in scientific research, particularly when scalable AI systems are applied to high-performance computing (HPC) platforms. This necessitates the integration of large-scale computational resources with expansive datasets to tackle complex scientific problems.
AI models like ChatGPT serve as exemplars of this transformative potential. The success of these models can…
Machine learning pioneer Hugging Face has launched Transformers version 4.42, a meaningful update to its well-regarded machine-learning library. Significant enhancements include the introduction of several advanced models, improved tool and retrieval-augmented generation support, GGUF fine-tuning, and quantized KV cache incorporation among other enhancements.
The release features the addition of new models like Gemma 2, RT-DETR, InstructBlip,…