A Survey of Long-Document Retrieval in the PLM and LLM Era

MCML Authors

Ercong Nie

→ Group Hinrich Schütze
 Computational Linguistics

Abstract

The proliferation of long-form documents presents a fundamental challenge to information retrieval (IR), as their length, dispersed evidence, and complex structures demand specialized methods beyond standard passage-level techniques. This survey provides the first comprehensive treatment of long-document retrieval (LDR), consolidating methods, challenges, and applications across three major eras. We systematize the evolution from classical lexical and early neural models to modern pre-trained (PLM) and large language models (LLMs), covering key paradigms like passage aggregation, hierarchical encoding, efficient attention, and the latest LLM-driven re-ranking and retrieval techniques. Beyond the models, we review domain-specific applications, specialized evaluation resources, and outline critical open challenges such as efficiency trade-offs, multimodal alignment, and faithfulness. This survey aims to provide both a consolidated reference and a forward-looking agenda for advancing long-document retrieval in the era of foundation models.

misc

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Preprint

Sep. 2025

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Authors

M. Li • M. Luo • T. Lv • Y. Zhang • S. Zhao • E. Nie • G. Zhou

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Links

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Research Area

 B2 | Natural Language Processing