SocArXiv
metadata only
RESEARCH SYNTHESIS -- LLM-Based Simulations of Survey Responses: A Systematic Literature Review and Synthesis of Methods
Large language models (LLMs) are increasingly used in survey research, including to predict, simulate, or impute responses. Despite a rapidly growing interest in applications to create synthetic survey data, the empirical evidence remains fragmented, with substantial variation in research objectives
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
- RESEARCH SYNTHESIS -- LLM-Based Simulations of Survey Responses: A Systematic Literature Review and Synthesis of Methods#1481271
- Bridging the Data Gap: LLM-Driven Synthetic Data Generation for Low-Resource Natural Language Inference#1120844
- Provenance Before Prose: Claim-Locked Reporting for Statistical Text Generation#1036765
- Toward Reproducible Research with LLMs: Guidance for Documentation, Reporting, and Transparency#1040277
- Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation#984882
Record · ID 1482255
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