From Policy Documents to Structured Survey Responses: Evaluating Large Language Models for Policy Monitoring
Science, technology, and innovation policies are crucial for competitiveness, yet their diversity and scale make them difficult to map and monitor consistently. Existing approaches rely heavily on manual survey efforts, which are costly and challenging to scale across countries. Large language models (LLMs) enable new possibilities for extracting and structuring information from long and unstructured policy documents. This paper presents an application of LLMs as "AI respondents" for generating structured survey responses from policy texts. We develop a data extraction pipeline based on long-context in-context learning to map information from public web sources into predefined survey cate...
| Authors | Carolyn Cole, Matthias Deschryvere, Toqeer Ehsan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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