Large Language Models (LLMs) have shown exceptional capabilities in complex reasoning tasks through recent advancements in scaling and specialized training approaches. While models like OpenAI o1 and ...

In large language models (LLMs), processing extended input sequences demands significant computational and memory resources, leading to slower inference and higher hardware costs. The attention ...

Adapting large language models for specialized domains remains challenging, especially in fields requiring spatial reasoning and structured problem-solving, even though they specialize in complex ...

Language models have become increasingly expensive to train and deploy. This has led researchers to explore techniques such as model distillation, where a smaller student model is trained to replicate ...

AI chatbots create the illusion of having emotions, morals, or consciousness by generating natural conversations that seem human-like. Many users engage with AI for chat and companionship, reinforcing ...

Large Language Models (LLMs) have gained significant importance as productivity tools, with open-source models increasingly matching the performance of their closed-source counterparts. These models ...

Large language models (LLMs) have demonstrated exceptional problem-solving abilities, yet complex reasoning tasks—such as competition-level mathematics or intricate code generation—remain challenging.

Most modern visualization authoring tools like Charticulator, Data Illustrator, and Lyra, and libraries like ggplot2, and VegaLite expect tidy data, where every variable to be visualized is a column ...

In recent years, the rapid scaling of large language models (LLMs) has led to extraordinary improvements in natural language understanding and reasoning capabilities. However, this progress comes with ...

Most modern visualization authoring tools like Charticulator, Data Illustrator, and Lyra, and libraries like ggplot2, and VegaLite expect tidy data, where every variable to be visualized is a column ...

Large Language Models (LLMs) have revolutionized natural language processing (NLP) but face significant challenges in practical applications due to their large computational demands. While scaling ...

LLMs have demonstrated exceptional capabilities, but their substantial computational demands pose significant challenges for large-scale deployment. While previous studies indicate that intermediate ...