update README

README.md

@@ -11,6 +11,7 @@ language:
 tags:
 - agent
 - opendata
+- open-government-data
 - ogd
 - gis
 - gpkg
@@ -27,4 +28,64 @@ configs:
   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.
+
+## 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