---
title: "Who cites Hierarchical Document — 16 citing papers"
description: "16 papers of the Elamur Research library cite «Hierarchical Document Refinement for Long-context Retrieval-augmented Generation», found…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Hierarchical Document Refinement for Long-context…](https://md.research.elamur.ai/free/en/papers/jin-2025-hierarchical-document-refinement-long-context/) / cited by

# Who cites «Hierarchical Document Refinement for Long-context Retrieval-augmented Generation»

16 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «Hierarchical Document Refinement for Long-context Retrieval-augmented Generation»](https://md.research.elamur.ai/free/en/papers/jin-2025-hierarchical-document-refinement-long-context/)

1. **[Search-o1: Agentic Search-Enhanced Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/li-2025-search-o1-agentic-search-enhanced/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaoxi Li, Guanting Dong, Jiajie Jin, et al.*2025 · 211 citations*
2. **[RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation](https://md.research.elamur.ai/free/en/papers/li-2025-retrollm-empowering-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoxi Li, Jiajie Jin, Yujia Zhou, et al.*2025 · 19 citations*
3. **[Progressive Multimodal Reasoning via Active Retrieval](https://md.research.elamur.ai/free/en/papers/dong-2025-progressive-multimodal-reasoning-active-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Chenghao Zhang, Mengjie Deng, et al.*2025 · 10 citations*
4. **[HiChunk: Evaluating and Enhancing Retrieval Augmented Generation with Hierarchical Chunking](https://md.research.elamur.ai/free/en/papers/lu-2026-hichunk-evaluating-enhancing-retrieval-augmented/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wensheng Lu, Keyu Chen, Zhifeng Shen, et al.*2026 · 4 citations*
5. **[Concept rather than Document: Context Compression via AMR-based Conceptual Entropy](https://md.research.elamur.ai/free/en/papers/shi-2026-concept-rather-than-document-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaize Shi, Xueyao Sun, Xiaohui Tao, et al.*2026*
6. **[From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning](https://md.research.elamur.ai/free/en/papers/wu-2026-experience-skill-multi-agent-generative/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Beining Wu, Fuyou Mao, Jiong Lin, et al.*2026*
7. **[HiGoE: Hierarchical Graph of Evidence to Enhance Retrieval-Augmented Generation for Long-context Summarization](https://md.research.elamur.ai/free/en/papers/yuan-2026-higoe-hierarchical-graph-evidence-enhance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Long Yuan, Kaiwen Tian, Zi Chen, et al.*2026*
8. **[Structured Dialogue Refinement: Building Retrieval-Augmented Question Answering on Goal-Oriented Dialogues](https://md.research.elamur.ai/free/en/papers/wu-2026-structured-dialogue-refinement-building-retrieval/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bin Wu, Sawan Kumar, Prasetya Ajie Utama, et al.*2026*

8 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/jin-2025-hierarchical-document-refinement-long-context/) — Hierarchical Document Refinement for Long-context Retrieval-augmented Generation