# Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction

arXiv: https://arxiv.org/abs/2608.08459 · HTML: https://mlsanity.com/paper/2608.08459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 55 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Builds on (most cited first, 30 of 33)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations](https://mlsanity.com/paper/2407.01523/) (2024)
- [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017)
- [LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating](https://mlsanity.com/paper/2412.18424/) (2024)
- [StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization](https://mlsanity.com/paper/2410.08815/) (2024)
- [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018)
- [DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing](https://mlsanity.com/paper/2410.12189/) (2024)
- [DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems](https://mlsanity.com/paper/2407.10701/) (2024)
- [Instruct and Extract: Instruction Tuning for On-Demand Information Extraction](https://mlsanity.com/paper/2310.16040/) (2023)
- [Benchmarking Retrieval-Augmented Multimodal Generation for Document Question Answering](https://mlsanity.com/paper/2505.16470/) (2025)
- [Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction](https://mlsanity.com/paper/2404.14215/) (2024)
- [Incremental Knowledge Base Construction Using DeepDive](https://mlsanity.com/paper/1502.00731/) (2015)
- [LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesis](https://mlsanity.com/paper/2409.18812/) (2024)
- [Structsum Generation for Faster Text Comprehension](https://mlsanity.com/paper/2401.06837/) (2024)
- [SemBench: A Benchmark for Semantic Query Processing Engines](https://mlsanity.com/paper/2511.01716/) (2025)
- [Map&Make: Schema Guided Text to Table Generation](https://mlsanity.com/paper/2505.23174/) (2025)
- [Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2205.14228/) (2022)
- [SQUiD: Synthesizing Relational Databases from Unstructured Text](https://mlsanity.com/paper/2505.19025/) (2025)
- [StructText: A Synthetic Table-to-Text Approach for Benchmark Generation with Multi-Dimensional Evaluation](https://mlsanity.com/paper/2507.21340/) (2025)
- [Long-Document QA with Chain-of-Structured-Thought and Fine-Tuned SLMs](https://mlsanity.com/paper/2603.29232/) (2026)

## Abstract (arXiv)

Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets. In domains such as finance, healthcare, education, transportation, and enterprise operations, downstream workflows rely on normalized schemas, entity identities, keys, cross-table relationships, and integrity constraints for analytics, compliance, auditing, and SQL-backed decision making. Existing Document-to-Table benchmarks are insufficient for this setting: flattening evidence into single tables can duplicate entities, obscure many-to-many relationships, create sparse records, and avoid testing whether extracted facts form a valid database instance. This creates an urgent need to evaluate document understanding as database construction rather than field extraction. We introduce Doc2DB-Bench, a benchmark for Document-to-Database construction, containing 203 long-document instances across 42 schemas and seven domain groups, with 117 entity tables, 132 relationship tables, 7,341 rows, and 41,935 cells. Built through a controllable DB-to-Doc synthesis pipeline and organized by a taxonomy of intra-table extraction and inter-table reasoning, the generated documents undergo authenticity verification, proving indistinguishable from real-world references. Doc2DB-Bench thus provides a testbed for reliable, auditable, and relationally faithful LLM-based data systems. The benchmark is publicly available at https://github.com/SetonLiang/Doc2DB-Bench.