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

LettuceDetect: Chinese Hallucination Detection Model

Model Name: lettucedect-610m-eurobert-cn-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 Chinese 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-CN (translated from the original RAGTruth dataset)
• Language: Chinese

How It Works

The model is trained to identify tokens in the Chinese 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-cn-v1",
    lang="cn",
    trust_remote_code=True
)

contexts = ["长城是中国古代的伟大防御工程，全长超过21,000公里。它的建造始于公元前7世纪，历经多个朝代。"]
question = "长城有多长？它是什么时候建造的？"
answer = "长城全长约50,000公里。它的建造始于公元前3世纪，仅在秦朝时期。"

# 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("预测:", predictions)

# 预测: [{'start': 4, 'end': 16, 'confidence': 0.89, 'text': '全长约50,000公里'}, {'start': 20, 'end': 41, 'confidence': 0.91, 'text': '建造始于公元前3世纪，仅在秦朝时期'}]

Performance

Results on Translated RAGTruth-CN

We evaluate our Chinese models on translated versions of the RAGTruth dataset. The EuroBERT-610M Chinese model achieves an F1 score of 77.27%, significantly outperforming prompt-based methods like GPT-4.1-mini (60.23%).

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

Language	Model	Precision (%)	Recall (%)	F1 (%)	GPT-4.1-mini F1 (%)	Δ F1 (%)
Chinese	EuroBERT-210M	75.46	73.38	74.41	60.23	+14.18
Chinese	EuroBERT-610M	78.90	75.72	77.27	60.23	+17.04

The 610M variant achieves significantly higher performance, with a +17.04% improvement over the GPT-4.1-mini baseline - the largest improvement among all languages. While this model requires more computational resources than the 210M variant, it delivers superior hallucination detection capability.

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}, 
}