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cs.CL2025
AutoBench: Automating LLM Evaluation through Reciprocal Peer Assessment
Dario Loi, Elena Maria MuiÃ, Federico Siciliano +4
We present AutoBench, a fully automated and self-sustaining framework for evaluating Large Language Models (LLMs) through reciprocal peer assessment. This paper provides a rigorous…
cs.CL2025
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…
cs.CL2024
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…