| --- |
| language: |
| - de |
| license: |
| - cc-by-nc-4.0 |
| task_categories: |
| - question-answering |
| tags: |
| - rag |
| - german |
| - the-dark-eye |
| - aventurien |
| - benchmark |
| - monolingual |
| - knowledge-base |
| pretty_name: Reasoning-Language Alignment QA Benchmark |
| size_categories: |
| - 10<n<100 |
| - 1k<n<10k |
| format: json |
| modality: |
| - text |
| --- |
| |
| # Reasoning-Language Alignment QA Benchmark |
|
|
| A **fully monolingual German** retrieval-augmented question-answering (RAG) benchmark over the fictional world of the tabletop role-playing game *The Dark Eye* (*Das Schwarze Auge*), set on the continent of **Aventurien**. The world is richly documented in German yet far too niche for large language models to answer from memory, so correct answers require reading and integrating retrieved evidence, making it a clean testbed for reasoning over retrieved context. |
|
|
| This dataset accompanies the paper: |
|
|
| > **Investigating the Role of Reasoning-Language Alignment in Monolingual Retrieval-Augmented Generation** — Oliver Hauck, Mario Sanz-Guerrero, Katharina von der Wense (2026) |
| > |
| > - Paper: <https://huggingface.co/papers/2610.03136> |
| > - Code (testbed + dataset creation pipeline): <https://github.com/soliver1/reason-in-the-language-you-retrieve> |
|
|
|
|
| ## ⚖️ Copyright & what is (not) included |
|
|
| - This benchmark covers the fictional world of *The Dark Eye* (© **Ulisses Spiele**). It is **not** an official Ulisses Spiele product and is published solely for research and reproducibility of the paper above. |
| - The raw source text of the region-description books is not included, as it is copyrighted. We publish only the *question*, a *reference answer*, and *derived metadata*. |
| - The reference answers are paraphrases written by the authors; in `qax` the gold key-facts are highlighted with `*…*`. |
| - If you own the books, the full pipeline (raw text, chunking, and RAG context) can be regenerated with the code in the linked repository. |
|
|
| ## Configurations (subsets) |
|
|
| | Config | Description | Rows | Book(s) | |
| |--------|-----------------------------------------------------------------------------------------------------------------------|-----:|---------| |
| | `qa2` | Curated QA set derived from book **G01** (*In den Dschungeln Meridianas*), with quality rating + human approval flags | 1,931 | G01 | |
| | `qax` | More complex, human-authored, multi-faceted questions | 30 | G01, G05, G08 | |
|
|
| ### `qa2` fields |
|
|
| | field | type | description | |
| |-------|------|-------------| |
| | `id` | str | Stable unique id (`qa2-<chapter>-<n>`) | |
| | `book` | str | Book code, e.g. `G01` | |
| | `chapter` | str | Source chapter code, e.g. `G01-012` | |
| | `question` | str | German question | |
| | `answer` | str | Reference answer (author paraphrase of the gold answer) | |
| | `rating` | int | LLM self-rating of the question's quality (1–5) | |
| | `reason` | str | LLM rationale for the rating | |
| | `user_rating` | bool | **Human-approval flag — `true` marks the final benchmark subset** | |
|
|
| ### `qax` fields |
|
|
| | field | type | description | |
| |-------|------|-------------| |
| | `id` | str | Stable unique id (`qax-<book>-<n>`) | |
| | `book` | str | Book code, e.g. `G01` | |
| | `question` | str | Complex German question | |
| | `answer` | str | Reference answer; gold key-facts highlighted with `*…*` | |
|
|
| ## Reproducing the paper's benchmark |
|
|
| The experiments use the **human-approved** subset of the curated set (`user_rating == true`), together with the complex set: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| curated = load_dataset("soliver1/reasoning-language-alignment", "qa2", split="test") |
| benchmark = curated.filter(lambda x: x["user_rating"]) # 585 approved questions |
| |
| complex_set = load_dataset("soliver1/reasoning-language-alignment", "qax", split="test") # 30 complex questions |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| qa2 = load_dataset("soliver1/reasoning-language-alignment", "qa2", split="test") |
| print(qa2[0]) |
| |
| qax = load_dataset("soliver1/reasoning-language-alignment", "qax", split="test") |
| print(qax[0]) |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the paper: |
|
|
| ```bibtex |
| @misc{hauck2026reasoninglanguage, |
| title = {Investigating the Role of Reasoning-Language Alignment in Monolingual Retrieval-Augmented Generation}, |
| author = {Hauck, Oliver and Sanz-Guerrero, Mario and von der Wense, Katharina}, |
| year = {2026}, |
| eprint = {2610.03136}, |
| archivePrefix = {arXiv}, |
| url = {https://huggingface.co/papers/2610.03136} |
| } |
| ``` |
|
|
| ## Trademark note |
|
|
| > ⓘ **DAS SCHWARZE AUGE**, **AVENTURIEN**, **DERE** und **THE DARK EYE** sind eingetragene Marken der Ulisses Spiele GmbH. Ohne vorherige schriftliche Genehmigung der Ulisses Medien und Spiel Distribution GmbH ist eine Verwendung der genannten Markenzeichen nicht gestattet. |
|
|
| ## License |
|
|
| `cc-by-nc-4.0` (Creative Commons Attribution-NonCommercial 4.0). |
|
|
| Derived from the world of *The Dark Eye*; not affiliated with or endorsed by Ulisses Spiele. |
|
|
