204 citations · 214 across the 5 of their papers we have counts for
6 papers
Redefining Retrieval Evaluation in the Era of LLMs
Giovanni Trappolini, Florin Cuconasu, Simone Filice +2
Traditional Information Retrieval (IR) metrics, such as nDCG, MAP, and MRR, assume that human users sequentially examine documents with diminishing attention to lower ranks. This a…
The Distracting Effect: Understanding Irrelevant Passages in RAG
Chen Amiraz, Florin Cuconasu, Simone Filice +1
A well-known issue with Retrieval Augmented Generation (RAG) is that retrieved passages that are irrelevant to the query sometimes distract the answer-generating LLM, causing it to…
Do RAG Systems Really Suffer From Positional Bias?
Florin Cuconasu, Simone Filice, Guy Horowitz +2
Retrieval Augmented Generation enhances LLM accuracy by adding passages retrieved from an external corpus to the LLM prompt. This paper investigates how positional bias - the tende…
A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems
Florin Cuconasu, Giovanni Trappolini, Nicola Tonellotto +1
Retrieval Augmented Generation (RAG) represents a significant advancement in artificial intelligence combining a retrieval phase with a generative phase, with the latter typically…
TEXT2TASTE: A Versatile Egocentric Vision System for Intelligent Reading Assistance Using Large Language Model
Wiktor Mucha, Florin Cuconasu, Naome A. Etori +2
The ability to read, understand and find important information from written text is a critical skill in our daily lives for our independence, comfort and safety. However, a signifi…
The Power of Noise: Redefining Retrieval for RAG Systems
Florin Cuconasu, Giovanni Trappolini, Federico Siciliano +5
Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with rel…