Large language models (LLMs) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in
addressing the challenges of Large Language Models (LLMs) without necessitating …

Gene set enrichment is a mainstay of functional genomics, but it relies on gene function
databases that are incomplete. Here we evaluate five large language models (LLMs) for …

The accuracy of life cycle assessment (LCA) studies is often questioned due to the two
grand challenges of life cycle inventory (LCI) modeling:(1) missing foreground flow data and …

Artificial intelligence (AI) methods are poised to revolutionize intellectual work, with
generative AI enabling automation of text analysis, text generation, and simple decision …

Large Language Models (LLMs) have demonstrated significant performance improvements
across various cognitive tasks. An emerging application is using LLMs to enhance retrieval …

Retrieval-augmented generation (RAG) is an umbrella of different components, design
decisions, and domain-specific adaptations to enhance the capabilities of large language …

Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging
external knowledge, a comprehensive evaluation of RAG systems is still challenging due to …

Despite the success of integrating large language models into the development of
conversational systems, many studies have shown the effectiveness of retrieving and …

In the constantly evolving field of cybersecurity, it is imperative for analysts to stay abreast of
the latest attack trends and pertinent information that aids in the investigation and attribution …