Large Language Models (LLMs) are versatile, yet they often falter in tasks requiring deep
and reliable reasoning due to issues like hallucinations, limiting their applicability in critical …

Large Language Models (LLMs) have demonstrated strong performance in handling
complex tasks requiring both extensive knowledge and reasoning abilities. However, the …

While training large language models (LLMs) from scratch can generate models with distinct
functionalities and strengths, it comes at significant costs and may result in redundant …

Large language models (LLMs) have demonstrated impressive language understanding
and generation capabilities, enabling them to answer a wide range of questions across …

The immense parameter space of Large Language Models (LLMs) endows them with
superior knowledge retention capabilities, allowing them to excel in a variety of natural …

As large language models (LLMs) are continuously being developed, their evaluation
becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub …

Large Language Models (LLMs) have shown remarkable capabilities, but their reasoning
abilities and underlying mechanisms remain poorly understood. We present a novel …

With the rapid development of NLP, large-scale language models (LLMs) excel in various
tasks across multiple domains now. However, existing benchmarks may not adequately …

Although large language models (LLMs) are impressive in solving various tasks, they can
quickly be outdated after deployment. Maintaining their up-to-date status is a pressing …

Understanding how large language models (LLMs) acquire, retain, and apply knowledge
remains an open challenge. This paper introduces a novel framework, K-(CSA)^ 2, which …