LLMs show significant biases in conflict event classification, with open-weight models exhibiting false illegitimation and adapted models showing actor bias and lexical sensitivity, making them unsuitable for unsupervised deployment.
AI in Social Good: LLM powered Interventions in Crisis Management and Disaster Response , volume =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Are LLMs Ready for Conflict Monitoring? Empirical Evidence from West Africa
LLMs show significant biases in conflict event classification, with open-weight models exhibiting false illegitimation and adapted models showing actor bias and lexical sensitivity, making them unsuitable for unsupervised deployment.