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What is Long Context Hallucination Detection about?

This document presents a novel architecture for detecting contextual hallucinations in long-context inputs generated by Large Language Models (LLMs). The authors construct a specialized dataset and propose a decomposition and aggregation mechanism that allows pre-trained models like BERT to effectively process long contexts, significantly improving detection performance and inference speed. The study highlights the limitations of existing methods and demonstrates the efficacy of their approach through exper

Author

SwetA Mishra

Language

EN