A weakly supervised pipeline combining few-shot prompting, data restructuring, LoRA fine-tuning, and ensemble voting reports 85.5% accuracy on the SHROOM hallucination detection task.
In: Proceedings of the 18th International Worksho p on Semantic Evalu- ation (SemEval-2024)
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Few-Shot Optimized Framework for Hallucination Detection in Resource-Limited NLP Systems
A weakly supervised pipeline combining few-shot prompting, data restructuring, LoRA fine-tuning, and ensemble voting reports 85.5% accuracy on the SHROOM hallucination detection task.