Large Language Models (LLMs) have shown significant promise in decision-making tasks
when fine-tuned on specific applications, leveraging their inherent common sense and …

Large language models (LLMs) are shown to benefit from chain-of-thought (COT) prompting,
particularly when tackling tasks that require systematic reasoning processes. On the other …

Retrieval Augmented Generation (RAG) expands the capabilities of modern large language
models (LLMs) in chatbot applications, enabling developers to adapt and personalize the …

Large language models (LLMs) have raised concerns about potential security threats
despite performing significantly in Natural Language Processing (NLP). Backdoor attacks …

The large language models (LLMs), which bridge the gap between human language
understanding and complex problem-solving, achieve state-of-the-art performance on …

Backdoor attacks present significant threats to Large Language Models (LLMs), particularly
with the rise of third-party services that offer API integration and prompt engineering …

Large language models (LLMs) have significantly transformed the landscape of Natural
Language Processing (NLP). Their impact extends across a diverse spectrum of tasks …

With rapid advances, generative large language models (LLMs) dominate various Natural
Language Processing (NLP) tasks from understanding to reasoning. Yet, language models' …

The open-sourcing of large language models (LLMs) accelerates application development,
innovation, and scientific progress. This includes both base models, which are pre-trained …

Large Language Models (LLMs) have demonstrated potential in cybersecurity applications
but have also caused lower confidence due to problems like hallucinations and a lack of …