arXiv (All)
open access
Large language model-generated patterns for epidemiological modeling: a multi-benchmark evaluation framework
Representing individual-level behavioral heterogeneity is challenging in epidemiological modeling, particularly when empirical behavioral data are sparse. We assess the potential of large language model (LLM)-generated behavioral outputs in addressing this challenge, using hepatitis C virus-related
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Self-reported archetypes and behavioral failures in Large Language Models#1110396
- Benchmarking Language Models for Statistical Problem Formulation#973398
- A systematic framework for generating novel experimental hypotheses from language models#128303
- Group Preference Collapse in Personalized Multimodal Large Language Models#1143853
- DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models#617827
Record · ID 1327584
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive