# Pre-training Methods in Information Retrieval

arXiv: https://arxiv.org/abs/2111.13853 · HTML: https://mlsanity.com/paper/2111.13853/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2021.

## Built on by (most cited first, 8 of 8)

- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy](https://mlsanity.com/paper/2406.11678/) (2024)
- [Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method](https://mlsanity.com/paper/2308.09861/) (2023)
- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [Multi-granular Adversarial Attacks against Black-box Neural Ranking Models](https://mlsanity.com/paper/2404.01574/) (2024)
- [No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users](https://mlsanity.com/paper/2410.07589/) (2024)
- [Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition](https://mlsanity.com/paper/2505.07166/) (2025)
- [GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models](https://mlsanity.com/paper/2607.24764/) (2026)

## Abstract (arXiv)

The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need. In recent years, the resurgence of deep learning has greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural information retrieval), especially the paradigm of pre-training methods (PTMs). Owing to sophisticated pre-training objectives and huge model size, pre-trained models can learn universal language representations from massive textual data, which are beneficial to the ranking task of IR. Recently, a large number of works, which are dedicated to the application of PTMs in IR, have been introduced to promote the retrieval performance. Considering the rapid progress of this direction, this survey aims to provide a systematic review of pre-training methods in IR. To be specific, we present an overview of PTMs applied in different components of an IR system, including the retrieval component, the re-ranking component, and other components. In addition, we also introduce PTMs specifically designed for IR, and summarize available datasets as well as benchmark leaderboards. Moreover, we discuss some open challenges and highlight several promising directions, with the hope of inspiring and facilitating more works on these topics for future research.