Quick Start

Detect Hallucinations

from lettucedetect.models.inference import HallucinationDetector

# Load a pre-trained model
detector = HallucinationDetector(
    method="transformer",
    model_path="KRLabsOrg/lettucedetect-base-modernbert-en-v1"
)

# Provide context, question, and answer
contexts = [
    "France is a country in Europe. The capital of France is Paris. "
    "The population of France is 67 million."
]
question = "What is the capital of France? What is the population?"
answer = "The capital of France is Paris. The population of France is 69 million."

# Get span-level predictions
predictions = detector.predict(
    context=contexts,
    question=question,
    answer=answer,
    output_format="spans"
)
print(predictions)
# [{'start': 31, 'end': 71, 'confidence': 0.99,
#   'text': ' The population of France is 69 million.'}]

Available Models

Model	Language	Context	Size
KRLabsOrg/lettucedetect-base-modernbert-en-v1	English	4K	149M
KRLabsOrg/lettucedetect-large-modernbert-en-v1	English	4K	395M
KRLabsOrg/lettucedetect-base-eurobert-multilingual-v1	7 languages	8K	210M

See Models for the full list.

Detection Methods

# Transformer-based (recommended for production)
detector = HallucinationDetector(method="transformer", model_path="...")

# LLM-based (uses OpenAI API)
detector = HallucinationDetector(method="llm", model_path="gpt-4o-mini")

# RAG Fact Checker (triplet-based)
detector = HallucinationDetector(method="rag_fact_checker", model_path="gpt-4o-mini")

Output Formats

# Span-level: exact character ranges of hallucinated text
predictions = detector.predict(..., output_format="spans")

# Sentence-level: which sentences contain hallucinations
predictions = detector.predict(..., output_format="sentences")