1 citations · 3 across the 5 of their papers we have counts for
5 papers
Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models
Alireza Salemi, Hamed Zamani
This paper introduces uRAG--a framework with a unified retrieval engine that serves multiple downstream retrieval-augmented generation (RAG) systems. Each RAG system consumes the r…
Evaluating Retrieval Quality in Retrieval-Augmented Generation
Alireza Salemi, Hamed Zamani
Evaluating retrieval-augmented generation (RAG) presents challenges, particularly for retrieval models within these systems. Traditional end-to-end evaluation methods are computati…
Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation
Alireza Salemi, Surya Kallumadi, Hamed Zamani
This paper studies retrieval-augmented approaches for personalizing large language models (LLMs), which potentially have a substantial impact on various applications and domains. W…
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering
Alireza Salemi, Juan Altmayer Pizzorno, Hamed Zamani
Knowledge-Intensive Visual Question Answering (KI-VQA) refers to answering a question about an image whose answer does not lie in the image. This paper presents a new pipeline for…
PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document Generation
Alireza Salemi, Amirhossein Abaskohi, Sara Tavakoli +2
Multilingual pre-training significantly improves many multilingual NLP tasks, including machine translation. Most existing methods are based on some variants of masked language mod…