---
license: mit
language:
- de
base_model:
- EuroBERT/EuroBERT-610m
pipeline_tag: token-classification
tags:
- token classification
- hallucination detection
- transformers
- question answer
datasets:
- KRLabsOrg/ragtruth-de-translated
---

# LettuceDetect: German Hallucination Detection Model

<p align="center">
  <img src="https://github.com/KRLabsOrg/LettuceDetect/blob/feature/cn_llm_eval/assets/lettuce_detective_multi.png?raw=true" alt="LettuceDetect Logo" width="400"/>
</p>

**Model Name:** KRLabsOrg/lettucedect-610m-eurobert-de-v1
**Organization:** KRLabsOrg  
**Github:** https://github.com/KRLabsOrg/LettuceDetect

## Overview

LettuceDetect is a transformer-based model for hallucination detection on context and answer pairs, designed for multilingual Retrieval-Augmented Generation (RAG) applications. This model is built on **EuroBERT-610M**, which has been specifically chosen for its extended context support (up to **8192 tokens**) and strong multilingual capabilities. This long-context capability is critical for tasks where detailed and extensive documents need to be processed to accurately determine if an answer is supported by the provided context.

**This is our German large model utilizing EuroBERT-610M architecture**

## Model Details

- **Architecture:** EuroBERT-610M with extended context support (up to 8192 tokens)
- **Task:** Token Classification / Hallucination Detection
- **Training Dataset:** RagTruth-DE (translated from the original RAGTruth dataset)
- **Language:** German

## How It Works

The model is trained to identify tokens in the German answer text that are not supported by the given context. During inference, the model returns token-level predictions which are then aggregated into spans. This allows users to see exactly which parts of the answer are considered hallucinated.

## Usage

### Installation

Install the 'lettucedetect' repository

```bash
pip install lettucedetect
```

### Using the model

```python
from lettucedetect.models.inference import HallucinationDetector

# For a transformer-based approach:
detector = HallucinationDetector(
    method="transformer", 
    model_path="KRLabsOrg/lettucedect-610m-eurobert-de-v1",
    lang="de",
    trust_remote_code=True
)

contexts = ["Frankreich ist ein Land in Europa. Die Hauptstadt von Frankreich ist Paris. Die Bevölkerung Frankreichs beträgt 67 Millionen."]
question = "Was ist die Hauptstadt von Frankreich? Wie groß ist die Bevölkerung Frankreichs?"
answer = "Die Hauptstadt von Frankreich ist Paris. Die Bevölkerung Frankreichs beträgt 69 Millionen."

# Get span-level predictions indicating which parts of the answer are considered hallucinated.
predictions = detector.predict(context=contexts, question=question, answer=answer, output_format="spans")
print("Predictions:", predictions)

# Predictions: [{'start': 41, 'end': 88, 'confidence': 0.9873546123504639, 'text': ' Die Bevölkerung Frankreichs beträgt 69 Millionen.'}]
```

## Performance

**Results on Translated RAGTruth-DE**

We evaluate our German models on translated versions of the [RAGTruth](https://aclanthology.org/2024.acl-long.585/) dataset. The EuroBERT-610M German model achieves an F1 score of 74.95%, significantly outperforming prompt-based methods like GPT-4.1-mini (60.91%) with a substantial improvement of +14.04 percentage points.

For detailed performance metrics across different languages, see the table below:

| Language | Model           | Precision (%) | Recall (%) | F1 (%) | GPT-4.1-mini F1 (%) | Δ F1 (%) |
|----------|-----------------|---------------|------------|--------|---------------------|----------|
| German  | EuroBERT-210M   | 66.70         | 66.70      | 66.70  | 60.91               | +5.79    |
| German  | EuroBERT-610M   | **77.04**     | **72.96**  | **74.95**  | 60.91               | **+14.04**   |

While the 610M variant requires more computational resources, it delivers substantially higher performance with over 8 percentage points improvement in F1 score compared to the 210M model.

### Manual Validation

We performed additional validation on a manually reviewed set of 300 examples covering all task types from the data (QA, summarization, data-to-text). The EuroBERT-610M German model showed strong performance with an F1 score of 71.79% on this curated dataset.

| Model         | Precision (%) | Recall (%) | F1 (%) |
|---------------|---------------|------------|--------|
| EuroBERT-210M | 68.32         | 68.32      | 68.32  |
| EuroBERT-610M | **74.47**     | 69.31      | **71.79** |

## Citing

If you use the model or the tool, please cite the following paper:

```bibtex
@misc{Kovacs:2025,
      title={LettuceDetect: A Hallucination Detection Framework for RAG Applications}, 
      author={Ádám Kovács and Gábor Recski},
      year={2025},
      eprint={2502.17125},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.17125}, 
}
```