The ability to generate coherent and contextually relevant text is increasingly important in a
variety of applications, prompting the need for more sophisticated language models. Our …

Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models
(LLMs) perform Question Answering (QA) tasks in various domains. However, RAG based …

Retrieval-enhanced machine learning (REML) refers to the use of information retrieval
methods to support reasoning and inference in machine learning tasks. Although relatively …

Introduction Frequent formative assessment is essential for accurately evaluating student
learning, enhancing engagement, and providing personalized feedback. In STEM …

Evaluating retrieval-augmented generation (RAG) systems remains challenging, particularly
for open-ended questions that lack definitive answers and require coverage of multiple sub …

Large Language Models (LLMs) have recently demonstrated a remarkable ability to model
time series data. These capabilities can be partly explained if LLMs understand basic time …

The world of Artificial Intelligence is rapidly evolving after the introduction of GEN AI. Artificial
Intelligence is being adopted in many fields and to automate complex works which frees up …

This letter evaluates the article by Gravina et al on ChatGPT's potential in providing medical
information for inflammatory bowel disease patients. While promising, it highlights the need …

E-commerce stores enable multilingual product discovery which require accurate product
title translation. Multilingual large language models (LLMs) have shown promising capacity …

Despite the rapid advancements in the field of Large Language Models (LLM), traditional
benchmarks have proven to be inadequate for assessing the performance of Retrieval …