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[2602.14743] LLMStructBench: Benchmarking Large Language Model Structured Data Extraction
Summary
LLMStructBench introduces a benchmark for evaluating large language models on structured data extraction, emphasizing the impact of prompting strategies on model performance.
Why It Matters
This research is significant as it provides a systematic approach to assess the capabilities of large language models in extracting structured data, which is crucial for applications in data processing and ETL tasks. By focusing on prompting strategies, it highlights an often-overlooked factor that can enhance model performance, especially for smaller models.
Key Takeaways
- LLMStructBench offers a novel benchmark for structured data extraction.
- Prompting strategies significantly influence model performance over size.
- The benchmark includes diverse scenarios for comprehensive evaluation.
- New performance metrics assess both token-level accuracy and document validity.
- Findings can guide future research in parsing and ETL applications.
Computer Science > Computation and Language arXiv:2602.14743 (cs) [Submitted on 16 Feb 2026] Title:LLMStructBench: Benchmarking Large Language Model Structured Data Extraction Authors:Sönke Tenckhoff, Mario Koddenbrock, Erik Rodner View a PDF of the paper titled LLMStructBench: Benchmarking Large Language Model Structured Data Extraction, by S\"onke Tenckhoff and 2 other authors View PDF HTML (experimental) Abstract:We present LLMStructBench, a novel benchmark for evaluating Large Language Models (LLMs) on extracting structured data and generating valid JavaScript Object Notation (JSON) outputs from natural-language text. Our open dataset comprises diverse, manually verified parsing scenarios of varying complexity and enables systematic testing across 22 models and five prompting strategies. We further introduce complementary performance metrics that capture both token-level accuracy and document-level validity, facilitating rigorous comparison of model, size, and prompting effects on parsing reliability. In particular, we show that choosing the right prompting strategy is more important than standard attributes such as model size. This especially ensures structural validity for smaller or less reliable models but increase the number of semantic errors. Our benchmark suite is an step towards future research in the area of LLM applied to parsing or Extract, Transform and Load (ETL) applications. Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) ACM classe...
