Moral knowledge retrieval improves Schwartz value detection more consistently than added context or larger models across tested conditions and model families.
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LaaB improves LLM hallucination detection by mapping self-judgment labels back into neural feature space and using mutual learning under logical consistency constraints between responses and meta-judgments.
SelfCheckGPT detects hallucinations by checking consistency across multiple sampled responses from black-box LLMs on WikiBio biography generation tasks.
DeBERTa-V3-base with focal loss, discourse features, and LLM-augmented data for minority classes achieves 0.76 Macro F1 on clarity-level classification of political QA pairs, ranking 8th in SemEval-2026 Task 6.
citing papers explorer
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More Context, Larger Models, or Moral Knowledge? A Systematic Study of Schwartz Value Detection in Political Texts
Moral knowledge retrieval improves Schwartz value detection more consistently than added context or larger models across tested conditions and model families.
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Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments
LaaB improves LLM hallucination detection by mapping self-judgment labels back into neural feature space and using mutual learning under logical consistency constraints between responses and meta-judgments.
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SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
SelfCheckGPT detects hallucinations by checking consistency across multiple sampled responses from black-box LLMs on WikiBio biography generation tasks.
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Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions
DeBERTa-V3-base with focal loss, discourse features, and LLM-augmented data for minority classes achieves 0.76 Macro F1 on clarity-level classification of political QA pairs, ranking 8th in SemEval-2026 Task 6.