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This paper proposes a validation framework for using Large Language Models to extract causal relations from social media posts during disasters. It evaluates the effectiveness of LLMs in identifying cause-effect relationships and compares them against expert-grounded reference graphs to assess reliability and risks.
This paper presents an empirical evaluation of LLM-guided semi-supervised learning for classifying social media crisis data. It demonstrates that LG-CoTrain outperforms classical baselines in low-resource settings and highlights the potential of transferring knowledge from LLMs to smaller, deployable models for disaster response.