Datasets:

michael7ma
/

ogd4all-benchmark

---
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 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](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