Can Large Language Models Reliably Code Qualitative Humanitarian Data? A Benchmark Study Against Human Expert Adjudication
arXiv:2606.26541v1 Announce Type: new Abstract: Data from affected populations are crucial for informing humanitarian response, but their value depends on timely and consistent interpretation of nuanced accounts of need. Humanitarian organizations often lack the staff, time, and specialist expertise required to analyze this information at scale. Large language models (LLMs) may expand this capacity, but their reliability for coding qualitative humanitarian data has not been directly established. This benchmark study compares 46 LLMs to a human Gold Standard using 150 high-fidelity synthetic hu
The proliferation of LLMs and increasing availability of qualitative data from humanitarian crises makes direct evaluation of LLM reliability crucial for practical application.
This study offers empirical evidence on the reliability of LLMs for sensitive, qualitative data analysis in humanitarian contexts, directly impacting resource allocation and trust in AI-driven insights.
The understanding of LLM capabilities for complex document analysis shifts from theoretical potential to benchmarked performance against human experts.
- · Humanitarian organizations
- · LLM developers (if models perform well)
- · Affected populations
- · Traditional qualitative data analysis service providers (potentially)
- · LLM developers (if models perform poorly)
Humanitarian organizations will adopt LLMs for qualitative data analysis, accelerating insights and response times.
This adoption could lead to new ethical guidelines and regulatory frameworks for AI use in sensitive humanitarian applications.
The successful integration of LLMs may redefine staffing needs and skill sets required for data analysis in the humanitarian sector, potentially freeing up human experts for higher-level tasks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG