KRLabsOrg/lettucedect-610m-eurobert-es-v1

LettuceDetect: Spanish Hallucination Detection Model

Model Name: lettucedetect-610m-eurobert-es-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 Spanish 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-ES (translated from the original RAGTruth dataset)
• Language: Spanish

How It Works

The model is trained to identify tokens in the Spanish 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

pip install lettucedetect

Using the model

from lettucedetect.models.inference import HallucinationDetector

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

contexts = ["Francia es un país de Europa. La capital de Francia es París. La población de Francia es de 67 millones."]
question = "¿Cuál es la capital de Francia? ¿Cuál es la población de Francia?"
answer = "La capital de Francia es París. La población de Francia es de 69 millones."

# 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("Predicciones:", predictions)

# Predicciones: [{'start': 33, 'end': 76, 'confidence': 0.9598274827003479, 'text': ' La población de Francia es de 69 millones.'}]

Performance

Results on Translated RAGTruth-ES

We evaluate our Spanish models on translated versions of the RAGTruth dataset. The EuroBERT-610M Spanish model achieves an F1 score of 73.25%, significantly outperforming prompt-based methods like GPT-4.1-mini (62.40%) with a substantial improvement of +10.85 percentage points.

For detailed performance metrics, see the table below:

Language	Model	Precision (%)	Recall (%)	F1 (%)	GPT-4.1-mini F1 (%)	Δ F1 (%)
Spanish	EuroBERT-210M	69.48	73.38	71.38	62.40	+8.98
Spanish	EuroBERT-610M	76.32	70.41	73.25	62.40	+10.85

The 610M model offers the best overall performance with nearly 2 percentage points improvement in F1 score compared to the 210M model. It particularly excels in precision, with a 76.32% precision rate that significantly reduces false positive hallucination detections.

Citing

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

@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}, 
}