This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

Abstract We introduce RAGAs (Retrieval Augmented Generation Assessment), a framework
for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. RAGAs is …

Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of
generated texts cannot be secured solely by the parametric knowledge they encapsulate …

Recently developed large language models have achieved remarkable success in
generating fluent and coherent text. However, these models often tend to'hallucinate'which …

Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating
up-to-date information, mitigating hallucinations, and enhancing response quality …

This survey summarises the most recent methods for building and assessing helpful, honest,
and harmless neural language models, considering small, medium, and large-size models …

Large language models (LLMs) have been significantly improved by instruction fine-tuning,
but still lack transparency and the ability to utilize up-to-date knowledge and information. In …

The prevalent use of large language models (LLMs) in various domains has drawn attention
to the issue of" hallucination," which refers to instances where LLMs generate factually …

Authoring data-driven articles is a complex process requiring authors to not only analyze
data for insights but also craft a cohesive narrative that effectively communicates the …

Retrieval-Augmented Generation (RAG) has been shown to enhance the factual accuracy of
Large Language Models (LLMs), but existing methods often suffer from limited reasoning …