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Min-Ji Kang

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#explainable ai Book Open access Oct 2026

Evaluating and explaining LLM classification for automatic screening in Systematic Reviews

This research conducts a set of experiments using open-source Large Language Models with small parameters hosted on local servers to screen titles and abstracts against pre-defined eligibility criteria respective to each topic, and suggests that AI may indeed aid with initial relevance screening, but more research is n...

Min-Ji Kang, Bipasha Banerjee, C. Comer · 0 citations
#large language models Book Open access Oct 2026

Evaluating and explaining LLM classification for automatic screening in Systematic Reviews

Evidence synthesis methods, such as systematic reviews (SR), require research teams to find, select, evaluate, and synthesize all available evidence to answer a specific research question in a reproducible and transparent, bias-reduced manner. Early in the process, teams must screen all unique titles and abstracts gath...

Min-Ji Kang, Bipasha Banerjee, C. Comer · 0 citations
#natural language process... Preprint Sep 2026

SWORD: Wikidata-based Distortions Reveal Hidden Cross-Lingual Inconsistencies in LLM Factual Error Rejection

This work introduces Systematic Wikidata-based Object-Relation Distortion (SWORD), a benchmark that evaluates whether models consistently reject factual errors across languages, and demonstrates that multilingual factual reasoning involves asymmetric capabilities that aggregate accuracy metrics systematically obscure.

Sanghyeok Park, Min-Ji Kang, Hosung Kwak et al. · 0 citations

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