Lecture Notes on Neural Information Retrieval
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Abstract (arXiv)
These lecture notes focus on the recent advancements in neural information retrieval, with particular emphasis on the systems and models exploiting transformer networks. These networks, originally proposed by Google in 2017, have seen a large success in many natural language processing and information retrieval tasks. While there are many fantastic textbook on information retrieval and natural language processing as well as specialised books for a more advanced audience, these lecture notes target people aiming at developing a basic understanding of the main information retrieval techniques and approaches based on deep learning. These notes have been prepared for a IR graduate course of the MSc program in Artificial Intelligence and Data Engineering at the University of Pisa, Italy.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jul 2022.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 1.
7 papers.
- 2023Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Methodcited by 29
- 2024FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generationcited by 19
- 2024Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systemscited by 9
- 2022The Infinite Index: Information Retrieval on Generative Text-To-Image Modelscited by 6
- 2024Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specificationscited by 3
- 2025Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisitioncited by 2
- 2023Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selectioncited by 1
- 2025Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition12 May 2025
- 2024Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specifications15 Oct 2024
- 2024Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems20 Feb 2024
- 2024FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation19 Feb 2024
- 2023Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection12 Sep 2023
- 2023Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method19 Aug 2023
- 2022The Infinite Index: Information Retrieval on Generative Text-To-Image Models14 Dec 2022