204 citations · 206 across the 3 of their papers we have counts for
4 papers · 1 filter
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…