Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

Manually integrating data of diverse formats and languages is vital to many artificial
intelligence applications. However, the task itself remains challenging and time-consuming …

Electronic health records (EHRs) house crucial patient data in clinical notes. As these notes
grow in volume and complexity, manual extraction becomes challenging. This work …

This survey presents an in-depth review of the transformative role of Natural Language
Processing (NLP) in finance, highlighting its impact on ten major financial applications:(1) …

The application of Large Language Models (LLMs) and Retrieval-Augmented Generation
(RAG) systems in the telecommunication domain presents unique challenges, primarily due …

Large Language Models (LLM) have shown remarkable language capabilities fueling
attempts to integrate them into applications across a wide range of domains. An important …

Retrieval Augmented Generation (RAG) has emerged as an effective solution for mitigating
hallucinations in Large Language Models (LLMs). The retrieval stage in RAG typically …

Large language models (LLMs) have demonstrated remarkable capabilities in document
processing, data analysis, and code generation. However, the generation of spatial …

The advancement of artificial intelligence algorithms has expanded their application to
several fields such as the biomedical domain. Artificial intelligence systems, including Large …

Retrieval-augmented text generation (RAG) addresses the common limitations of large
language models (LLMs), such as hallucination, by retrieving information from an updatable …