---
dataset_info:
- config_name: fwp
  features:
  - name: wine
    dtype: large_string
  - name: recipe
    dtype: large_string
  - name: true_label
    dtype: large_string
  - name: wine_url
    dtype: large_string
  splits:
  - name: test
    num_bytes: 177423
    num_examples: 1000
  download_size: 49545
  dataset_size: 177423
- config_name: wfc
  features:
  - name: url
    dtype: string
  - name: title
    dtype: string
  - name: type
    dtype: string
  - name: sugar
    dtype: float64
  - name: alcohol
    dtype: float64
  - name: country
    dtype: string
  - name: region
    list: string
  - name: grapes
    list: string
  - name: dryness
    dtype: string
  - name: acidity
    dtype: string
  - name: body
    dtype: string
  splits:
  - name: test
    num_bytes: 239557
    num_examples: 1000
  download_size: 115685
  dataset_size: 239557
- config_name: wtqa
  features:
  - name: question
    dtype: large_string
  - name: a
    dtype: large_string
  - name: b
    dtype: large_string
  - name: c
    dtype: large_string
  - name: d
    dtype: large_string
  - name: level
    dtype: large_string
  - name: true_label
    dtype: large_string
  - name: language
    dtype: large_string
  splits:
  - name: test
    num_bytes: 193779
    num_examples: 1024
  download_size: 74439
  dataset_size: 193779
configs:
- config_name: fwp
  data_files:
  - split: test
    path: fwp/test-*
- config_name: wfc
  data_files:
  - split: test
    path: wfc/test-*
- config_name: wtqa
  data_files:
  - split: test
    path: wtqa/test-*
license: cc-by-nc-4.0
task_categories:
- question-answering
- text-classification
language:
- en
- da
- de
- es
- fi
- it
- sk
- sv
tags:
- wine
- sommelier
- benchmark
pretty_name: SommBench
size_categories:
- 1K<n<10K
---

# SommBench

**SommBench** is a multilingual benchmark for assessing sommelier expertise in large language models. It comprises 3,024 expert-curated examples across eight languages (da, de, en, es, fi, it, sk, sv), designed by professional sommeliers to evaluate sensory grounding, factual wine knowledge, and practical pairing skills through three tasks: WTQA, WFC, and FWP.

## Configs

### `wtqa` — Wine Theory Question Answering (1,024 examples)
Multiple-choice questions about wine knowledge at varying difficulty levels.

| Column | Type | Description |
|--------|------|-------------|
| `question` | `string` | The question text |
| `a` | `string` | Answer choice A |
| `b` | `string` | Answer choice B |
| `c` | `string` | Answer choice C |
| `d` | `string` | Answer choice D |
| `level` | `string` | Difficulty level (Level 1–4) |
| `true_label` | `string` | Correct answer key (a/b/c/d) |
| `language` | `string` | Language code (da, de, en, es, fi, it, sk, sv) |

### `wfc` — Wine Feature Completion (1,000 examples)
Complete missing attributes of a wine profile from partial metadata using structured generation.

| Column | Type | Description |
|--------|------|-------------|
| `url` | `string` | Source URL |
| `title` | `string` | Wine name/vintage |
| `type` | `string` | Wine type (red, white, …) |
| `sugar` | `float64` | Sugar content (g/L) |
| `alcohol` | `float64` | Alcohol percentage |
| `country` | `string` | Country of origin |
| `region` | `list[string]` | Wine region(s) |
| `grapes` | `list[string]` | Grape variety/varieties |
| `dryness` | `string` | Dryness classification |
| `acidity` | `string` | Acidity classification |
| `body` | `string` | Body classification |

### `fwp` — Food-Wine Pairing (1,000 examples)
Binary classification of whether a wine pairs well with a given recipe.

| Column | Type | Description |
|--------|------|-------------|
| `wine` | `string` | Wine name |
| `recipe` | `string` | Recipe / dish description |
| `true_label` | `string` | Pairing label (yes/no) |
| `wine_url` | `string` | Vivino/Alko URL for the wine |

## Usage

```python
from datasets import load_dataset

wtqa = load_dataset("sommify/test", "wtqa", split="test")
wfc  = load_dataset("sommify/test", "wfc",  split="test")
fwp  = load_dataset("sommify/test", "fwp",  split="test")
```

## Citation

If you use SommBench in your research, please cite:

```bibtex
@misc{brach2026sommbenchassessingsommelierexpertise,
      title={SommBench: Assessing Sommelier Expertise of Language Models},
      author={William Brach and Tomas Bedej and Jacob Nielsen and Jacob Pichna and Juraj Bedej and Eemeli Saarensilta and Julie Dupouy and Gianluca Barmina and Andrea Blasi Núñez and Peter Schneider-Kamp and Kristian Košťál and Michal Ries and Lukas Galke Poech},
      year={2026},
      eprint={2603.12117},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.12117}
}
```
## Links

- **Paper:** [SommBench: Assessing Sommelier Expertise of Language Models](https://arxiv.org/abs/2603.12117)
- **GitHub:** [https://github.com/sommify/sommbench](https://github.com/sommify/sommbench)