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arXiv AI By Yongxin Deng (University of Technology Sydney), Zhen Fang (University of Technology Sydney), Guansong Pang (Singapore Management University), Sharon Li (University of Wisconsin-Madison), Ling Chen (University of Technology Sydney)

CHORD: Cross-Model Hallucination Detection via Relational Graph Discrimination

Read the original on arXiv AI →

The paper introduces CHORD, a relational graph discrimination framework designed to detect hallucinations across different large language models (LLMs). It addresses the challenge of cross-model representation heterogeneity by modeling hidden states as joint relational graphs over layers, tokens, and features, and employs relation-aware attention and meta-learning to extract transferable detection signals. Experiments demonstrate that CHORD outperforms existing methods in cross-model generalization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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