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NLPCC 2026 Task 10: Citation-Level Faithfulness Verification with DeBERTa Ensembles and Class-Wise Calibration

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

This paper presents our system for Track 2 of the NLPCC 2026 Shared Task 10 on citation-level faithfulness in AI-assisted scientific reporting. Given an atomic scientific claim and the structured full text of its cited paper, the task requires both a four-way relation label and up to three evidence paragraph identifiers. The label head ensembles a paragraph-aware cross-encoder with a document-level DeBERTa-large classifier, followed by class-wise decision calibration. Probability-level fusion is motivated by an out-of-fold tendency to over-predict Topical Match. The evidence head combines para

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-09-19T05:18:19.000Z

First collected: 2026-09-23T12:01:45.602Z. This is not the publication date.