Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.08298 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908644496375808 |
|---|---|
| author | Bindini, Luca Giovannini, Simone Marinai, Simone Nardoni, Valeria Ali, Kimiya Noor |
| author_facet | Bindini, Luca Giovannini, Simone Marinai, Simone Nardoni, Valeria Ali, Kimiya Noor |
| contents | This work investigates the ability of Vision Large Language Models (VLLMs) to understand and interpret the structure of tables in scientific articles. Specifically, we explore whether VLLMs can infer the hierarchical structure of tables without additional processing. As a basis for our experiments we use the PubTables-1M dataset, a large-scale corpus of scientific tables. From this dataset, we extract a subset of tables that we introduce as Complex Hierarchical Tables (CHiTab): a benchmark collection of complex tables containing hierarchical headings. We adopt a series of prompt engineering strategies to probe the models' comprehension capabilities, experimenting with various prompt formats and writing styles. Multiple state-of-the-art open-weights VLLMs are evaluated on the benchmark first using their off-the-shelf versions and then fine-tuning some models on our task. We also measure the performance of humans to solve the task on a small set of tables comparing with performance of the evaluated VLLMs. The experiments support our intuition that generic VLLMs, not explicitly designed for understanding the structure of tables, can perform this task. This study provides insights into the potential and limitations of VLLMs to process complex tables and offers guidance for future work on integrating structured data understanding into general-purpose VLLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical structure understanding in complex tables with VLLMs: a benchmark and experiments Bindini, Luca Giovannini, Simone Marinai, Simone Nardoni, Valeria Ali, Kimiya Noor Computation and Language This work investigates the ability of Vision Large Language Models (VLLMs) to understand and interpret the structure of tables in scientific articles. Specifically, we explore whether VLLMs can infer the hierarchical structure of tables without additional processing. As a basis for our experiments we use the PubTables-1M dataset, a large-scale corpus of scientific tables. From this dataset, we extract a subset of tables that we introduce as Complex Hierarchical Tables (CHiTab): a benchmark collection of complex tables containing hierarchical headings. We adopt a series of prompt engineering strategies to probe the models' comprehension capabilities, experimenting with various prompt formats and writing styles. Multiple state-of-the-art open-weights VLLMs are evaluated on the benchmark first using their off-the-shelf versions and then fine-tuning some models on our task. We also measure the performance of humans to solve the task on a small set of tables comparing with performance of the evaluated VLLMs. The experiments support our intuition that generic VLLMs, not explicitly designed for understanding the structure of tables, can perform this task. This study provides insights into the potential and limitations of VLLMs to process complex tables and offers guidance for future work on integrating structured data understanding into general-purpose VLLMs. |
| title | Hierarchical structure understanding in complex tables with VLLMs: a benchmark and experiments |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.08298 |