| | --- |
| | license: mit |
| | task_categories: |
| | - question-answering |
| | - table-question-answering |
| | language: |
| | - de |
| | - en |
| | - fr |
| | - it |
| | tags: |
| | - agent |
| | - opendata |
| | - open-government-data |
| | - ogd |
| | - gis |
| | - gpkg |
| | - csv |
| | - rag |
| | - zurich |
| | - llm |
| | - geospatial |
| | pretty_name: OGD4All Benchmark |
| | size_categories: |
| | - n<1K |
| | configs: |
| | - config_name: benchmark |
| | data_files: |
| | - split: test |
| | path: benchmarks/benchmark_german.jsonl |
| | --- |
| | |
| | # OGD4All Benchmark [](https://opensource.org/licenses/MIT) |
| |
|
| | This is a 199-question benchmark that was used to evaluate the overall performance of OGD4All and different configurations (LLM, orchestration, ...). |
| | OGD4All is an LLM-based prototype system enabling an easy-to-use, transparent interaction with Geospatial Open Government Data through natural language. |
| | Each question requires GIS, SQL and/or topological operations on zero, one, or multiple datasets in GPKG or CSV formats to be answered. |
| |
|
| | ## Tasks |
| | The benchmark can be used to evaluate systems against two main tasks: |
| |
|
| | 1. **Dataset Retrieval**: Given actual metadata of 430 City of Zurich datasets and a question, identify the subset of $k$ relevant datasets to answer the question. |
| | Note that the case $k=0$ is included |
| |
|
| | 2. **Dataset Analysis**: Given a set of *relevant* datasets, corresponding metadata and a question, appropriately process these datasets (e.g. via generated Python code snippets) and produce a textual answer. Note that OGD4All can accompany this answer with an interactive map, plots and/or tables, but only the textual answer is evaluated. |
| |
|
| | ## Evaluation |
| | ### Metrics |
| | | Metric | Description | |
| | |---|---| |
| | | Recall | Percentage of relevant datasets that were retrieved. | |
| | | Precision | Percentage of retrieved datasets that are relevant. | |
| | | Answerability | Accuracy of classifying whether a question can be answered with the available data or not. | |
| | | Correctness | Whether the final answer matches the ground-truth answer. | |
| | | Latency | Time elapsed from query submission to dataset output (retrieval) or from dataset submission to final answer (analysis). | |
| | | Token Consumption | Total number of consumed tokens in the retrieval or analysis stage. Can be distinguished into input, output, and reasoning tokens. | |
| | | API Cost | Total cost of retrieval or analysis stage. | |
| |
|
| | ### Dataset Retrieval |
| | To evaluate dataset retrieval, rely on the `"relevant_datasets"` list in the `"outputs"` dict, which gives you the list of relevant titles. |
| | You can map between metadata files and titles using the `data/dataset_title_to_file.csv`. |
| |
|
| | ### Dataset Analysis |
| | To evaluate dataset analysis, provide your architecture with relevant datasets specified in the `"outputs"` dict and the question, then either manually compare the generated answer with the ground-truth answer in the `"outputs"` dict, or use the LLM judge system prompt given in `eval_prompts/LLM_JUDGE_SYSTEM_PROMPT.txt`, with the question, reference and predicted answer provided via a subsequent user message. |
| |
|
| | > [!NOTE] |
| | > A few questions were found to have multiple options of valid relevant datasets, and also multiple valid answers. Therefore, your evaluation should consider the attributes `alternative_relevant_datasets` and `alternative_answer` if present. |
| | |
| | ## Benchmark Notes |
| | - **benchmark_german.jsonl** is the main benchmark, developed in German. All metadata/datasets are always in German. |
| | - We further provide automatically-translated versions of the questions (via DeepL API) in benchmark_english.jsonl, benchmark_french.jsonl, and benchmark_italian.jsonl. |
| | - benchmark_template.jsonl is the template that was used for generating the previously mentioned benchmark, with templated questions that can be instantiated with different arguments. |
| | - `benchmarks/gt_scripts` contains Python files that were hand-written to generate the ground-truth answer for each question that has relevant datasets. The filename corresponds to the benchmark entry ID. |
| | - The city of Zurich datasets are under CC-0 license. Recent versions can be downloaded [here](https://www.stadt-zuerich.ch/geodaten/), but for evaluation you should use the included datasets, as some answers might change otherwise. |
| |
|
| | ## Citation |
| | If you use this benchmark in your research, gladly cite our accompanying paper: |
| | ``` |
| | @article{siebenmann_ogd4all_2025, |
| | archivePrefix = {arXiv}, |
| | arxivId = {2602.00012}, |
| | author = {Siebenmann, Michael and S{\'a}nchez-Vaquerizo, Javier Argota and Arisona, Stefan and Samp, Krystian and Gisler, Luis and Helbing, Dirk}, |
| | journal = {arXiv preprint arXiv:2602.00012}, |
| | month = {nov}, |
| | title = {{OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models}}, |
| | url = {https://arxiv.org/abs/2602.00012}, |
| | year = {2025} |
| | } |
| | ``` |
| |
|
| | Next to the benchmark, this paper (accepted at IEEE CAI 2026) introduces the OGD4All architecture, which achieves high recall and correctness scores, even with "older" frontier models such as GPT-4.1. |
| | OGD4All's source code is publicly available: https://github.com/ethz-coss/ogd4all |
| |
|
