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

• URL: http://arxiv.org/abs/2509.07759v2
• Date: Sat, 25 Oct 2025 13:44:18 GMT
• Title: A Survey of Long-Document Retrieval in the PLM and LLM Era
• Authors: Minghan Li, Miyang Luo, Tianrui Lv, Yishuai Zhang, Siqi Zhao, Ercong Nie, Guodong Zhou,
• Abstract summary: This survey provides the first comprehensive treatment of long-document retrieval (LDR)<n>We systematize the evolution from classical lexical and early neural models to modern pre-trained (PLM) and large language models (LLMs)<n>We review domain-specific applications, specialized evaluation resources, and outline critical open challenges such as efficiency trade-offs, multimodal alignment, and faithfulness.
• Score: 19.07164308496093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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