4 citations · 5 across the 4 of their papers we have counts for
4 papers
Quantifying Retriever-Generator Alignment in RAG with Local Explanations
Korbinian Randl, Guido Rocchietti, Aron Henriksson +3
Retrieval-Augmented Generation (RAG) systems combine dense retrievers and language models to ground outputs in external documents. However, the interaction between these components…
Human Mobility Datasets Enriched With Contextual and Social Dimensions
Chiara Pugliese, Francesco Lettich, Guido Rocchietti +2
In this resource paper, we present two publicly available datasets of semantically enriched human trajectories, together with the pipeline to build them. The trajectories are publi…
Efficient Conversational Search via Topical Locality in Dense Retrieval
Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego +2
Pre-trained language models have been widely exploited to learn dense representations of documents and queries for information retrieval. While previous efforts have primarily focu…
Rewriting Conversational Utterances with Instructed Large Language Models
Elnara Galimzhanova, Cristina Ioana Muntean, Franco Maria Nardini +2
Many recent studies have shown the ability of large language models (LLMs) to achieve state-of-the-art performance on many NLP tasks, such as question answering, text summarization…