One of the biggest hurdles organizations face is implementing Large Language Models (LLMs) to handle intricate workflows effectively. Issues of speed, flexibility, and scalability often hinder the automation of complex workflows requiring coordination across multiple systems. Enterprises struggle with the cumbersome nature of configuring LLMs for seamless collaboration across data sources, making it challenging to…
Large Language Models (LLMs) have demonstrated remarkable proficiency in In-Context Learning (ICL), which is a technique that teaches them to complete tasks using just a few examples included in the input prompt and no further training. One of the primary features of ICL is that these models can manage several computationally different ICL tasks simultaneously…
There is a growing demand for embedding models that balance accuracy, efficiency, and versatility. Existing models often struggle to achieve this balance, especially in scenarios ranging from low-resource applications to large-scale deployments. The need for more efficient, high-quality embeddings has driven the development of new solutions to meet these evolving requirements. Overview of Sentence Transformers…
There is a need for flexible and efficient adaptation of large language models (LLMs) to various tasks. Existing approaches, such as mixture-of-experts (MoE) and model arithmetic, struggle with requiring substantial tuning data, inflexible model composition, or strong assumptions about how models should be used. These limitations call for a methodology that can adapt LLMs efficiently…
Artificial Intelligence is evolving significantly, and Large Language Models have shown a remarkable capacity to comprehend human-text inputs. Going beyond simple text to analyzing and generating code, LLMs have shown promising results in software development. However, with increased complexity, providing a quality assessment of the code becomes challenging. This paper aims to present CodeJudge, which…
Mobile Vehicle-to-Microgrid (V2M) services enable electric vehicles to supply or store energy for localized power grids, enhancing grid stability and flexibility. AI is crucial in optimizing energy distribution, forecasting demand, and managing real-time interactions between vehicles and the microgrid. However, adversarial attacks on AI algorithms can manipulate energy flows, disrupting the balance between vehicles and…
Widely growing sectors, like Healthcare, logistics, and smart cities, are interconnected on devices that require task reasoning capabilities in the Internet of Things (IoT) systems. This requirement has prompted researchers to find effective ways to integrate real-time data and contextual understanding into Large Language Models (LLMs), which have difficulty interpreting real-world tasks. LLMs process IoT…