Papers with DHP
DocHieNet: A Large and Diverse Dataset for Document Hierarchy Parsing (2024.emnlp-main)
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| Challenge: | Existing methods for document hierarchy parsing are limited due to the small scale and inconsistency of datasets. |
| Approach: | They propose a document hierarchy parsing dataset to compensate for the data scarcity problem and propose 'dHP' framework to grasp fine-grained text content and coarse-grounded pattern at layout element level. |
| Outcome: | The proposed framework grasps both fine-grained text content and coarse-grounded pattern at layout element level, enhancing the capacity of pre-trained text-layout models in handling multi-page and multi-level challenges. |
DHP Benchmark: Are LLMs Good NLG Evaluators? (2025.findings-naacl)
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Yicheng Wang, Jiayi Yuan, Yu-Neng Chuang, Zhuoer Wang, Yingchi Liu, Mark Cusick, Param Kulkarni, Zhengping Ji, Yasser Ibrahim, Xia Hu
| Challenge: | Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks. |
| Approach: | They propose a framework that measures the discernment of Large Language Models (LLMs) across diverse NLG tasks. |
| Outcome: | The proposed framework provides quantitative discernment scores for LLMs across four NLG tasks. |
HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering (2026.acl-long)
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| Challenge: | Existing approaches to document-based Opendomain Question Answering (ODQA) use flat text chunks or page-level images to locate the correct document. |
| Approach: | They propose a hierarchical tree-based multimodal retrieval framework that elevates document hierarchy to a first-class retrieval signal. |
| Outcome: | The proposed framework outperforms page- and chunk-based baselines on ODQA benchmarks and improves retrieval recall by 12.9% and end-to-end QA performance by 6.8%. |