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LettuceDetect: A Hallucination Detection Framework for RAG Applications
- Large Language Models (LLMs) have advanced NLP tasks, but hallucinations remain a challenge in critical domains like healthcare and legal settings.
- Retrieval-Augmented Generation (RAG) aims to reduce hallucinations by grounding LLM responses in retrieved documents.
- LettuceDetect, using ModernBERT, detects hallucinations in RAG applications efficiently and outperforms older BERT-based models.
- RAGTruth is a benchmark for evaluating hallucination detection in RAG settings, providing annotated examples and spans.
- LettuceDetect utilizes token-level classification for hallucination detection, achieving competitive performance with lower computational costs.
- The models are trained on the RAGTruth dataset and perform inference by detecting hallucinations at the token and span levels.
- LettuceDetect demonstrates strong performance in hallucination detection, surpassing other models and achieving state-of-the-art span-level results.
- The models are efficient, processing 30-60 examples per second on a single NVIDIA A100 GPU, suitable for real-time and resource-constrained environments.
- Overall, LettuceDetect offers accurate hallucination detection with lean, purpose-built encoder-based models for RAG systems.
- The framework provides a foundation for future research in expanding to new datasets, languages, and exploring advanced architectures.
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