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 ...
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 ...
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 ...
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 model (LLM)–based AI companions have evolved from simple chatbots into entities that users perceive as friends, partners, or even family members. Yet, despite their human-like ...
Reasoning tasks are yet a big challenge for most of the language models. Instilling a reasoning aptitude in models, particularly for programming and mathematical applications that require solid ...
Test-Time Scaling (TTS) is a crucial technique for enhancing the performance of LLMs by leveraging additional computational resources during inference. Despite its potential, there has been little ...
Competitive programming has long served as a benchmark for assessing problem-solving and coding skills. These challenges require advanced computational thinking, efficient algorithms, and precise ...
Multi-agent AI systems utilizing LLMs are increasingly adept at tackling complex tasks across various domains. These systems comprise specialized agents that collaborate, leveraging their unique ...
The dominant approach to pretraining large language models (LLMs) relies on next-token prediction, which has proven effective in capturing linguistic patterns. However, this method comes with notable ...
Artificial intelligence models face a fundamental challenge in efficiently scaling their reasoning capabilities at test time. While increasing model size often leads to performance gains, it also ...