Retrieval-Augmented Generation (RAG) allows overcoming the limited knowledge of LLMs
by extending the input with external information. As a consequence, the contextual inputs to …

Retrieval-augmented generation improves various aspects of large language models
(LLMs) generation, but suffers from computational overhead caused by long contexts as well …

Large Language Models (LLMs) are transforming how people find information, and many
users turn nowadays to chatbots to obtain answers to their questions. Despite the instant …

Retrieval-Augmented Generation (RAG) pipelines enhance Large Language Models (LLMs)
by retrieving relevant documents, but they face scalability issues due to high inference costs …

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 …