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20242026
most citedTell Me Why: Explainable Public Health Fact-Checking with Large Language Models

4 citations · 4 across the 4 of their papers we have counts for

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cs.CL2026

Consensus Measures for Unstructured Biomedical Text Annotations

Pascal Wullschleger, Christian Kreis, Martin A. Walter +2

Biomedical literature is increasingly mined for knowledge beyond the questions it was written to answer. Because the target concepts are not known in advance, annotators prefer ope…

cs.CL2025

FoodTaxo: Generating Food Taxonomies with Large Language Models

Pascal Wullschleger, Majid Zarharan, Donnacha Daly +2

We investigate the utility of Large Language Models for automated taxonomy generation and completion specifically applied to taxonomies from the food technology industry. We explor…

cs.CL2025

Reference-Free Evaluation of Taxonomies

Pascal Wullschleger, Majid Zarharan, Donnacha Daly +2

We introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. The first metric evaluates robustness by calculating the correlation between…

cs.CL2024

FarExStance: Explainable Stance Detection for Farsi

Majid Zarharan, Maryam Hashemi, Malika Behroozrazegh +3

We introduce FarExStance, a new dataset for explainable stance detection in Farsi. Each instance in this dataset contains a claim, the stance of an article or social media post tow…

cs.CL2024★ 4 cited

Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models

Majid Zarharan, Pascal Wullschleger, Babak Behkam Kia +2

This paper presents a comprehensive analysis of explainable fact-checking through a series of experiments, focusing on the ability of large language models to verify public health…