A Survey of Long-Document Retrieval in the PLM and LLM Era
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911230732533760 |
|---|---|
| author | Li, Minghan Luo, Miyang Lv, Tianrui Zhang, Yishuai Zhao, Siqi Nie, Ercong Zhou, Guodong |
| author_facet | Li, Minghan Luo, Miyang Lv, Tianrui Zhang, Yishuai Zhao, Siqi Nie, Ercong Zhou, Guodong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Long-Document Retrieval in the PLM and LLM Era Li, Minghan Luo, Miyang Lv, Tianrui Zhang, Yishuai Zhao, Siqi Nie, Ercong Zhou, Guodong Information Retrieval 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. |
| title | A Survey of Long-Document Retrieval in the PLM and LLM Era |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2509.07759 |