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Mark Stevenson

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#artificial intelligence Preprint Sep 2026

Evaluating and Improving Evidence-Grounded Fact-Checking in LLMs via Multi-Round Evidence Ablation

Fact-Ablated Evaluation (FAE) is introduced, a new evaluation framework that iteratively ablates the cited evidence to assess whether LLMs revise their predictions accordingly and highlights that strong fact-checking performance can still coexist with weak evidence dependency, while REAL encourages veracity predictions...

Xing-Yu Deng, Mingzi Cao, Nikolaos Aletras et al. · 0 citations
Book Open access Jul 2026

Rank-ICL: Ranking-based In-context Learning for Search Result Explanation

Evaluation on WikiSA and ExaRank shows that ranking-based few-shot prompting generally improves over zero-shot prompting and achieves competitive performance against random-shot prompting, indicating that retrieval-based demonstration selection is beneficial but not uniformly superior in all settings.

A. Laksito, Aali Alqarni, Mark Stevenson · 0 citations

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