Pre-Generation Hallucination Detection in Large Language Models via Soft-Target Attention Probing

The article introduces a novel approach for pre-generation hallucination detection in large language models, framing it as a risk-estimation problem rather than binary classification. It employs soft-target supervision based on empirical answer error rates and adapts attention probing to selectively aggregate relevant prompt representations, demonstrating improved performance across three question-answering benchmarks and five models. This method is significant for practitioners as it enhances the ability to mitigate hallucination risks before generation, potentially improving the reliability of LLM outputs.