LLM Hallucination Detector Model
This is a fine-tuned version of Microsoft's DeBERTa-v3-base trained on 25,000+ examples of the FEVER NLI dataset.
It is designed to evaluate whether an AI-generated statement (Claim) is supported, contradicted, or unresolvable based on a provided context paragraph (Evidence).
Output Labels
- LABEL_0 (FACTUAL): The claim is supported by the evidence.
- LABEL_1 (UNCERTAIN): The evidence is neutral or does not contain enough information to verify the claim.
- LABEL_2 (HALLUCINATION): The claim is refuted/contradicted by the evidence.
Hyperparameters
- Epochs: 3
- Batch Size: 8
- Learning Rate: 2e-5
- Optimizer: AdamW
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