So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis
arXiv 2607.11890•02312e2a27969aaeb51e5da98f1bd88b4e8f329ff9daaad1de57d0d80250ea05
AI-governanceAI-insuranceAI-riskCBRNacademic-misconductagent-economycatastrophic-riskcontent-moderationcritical-infrastructuredual-submissionsgenerative-AIlarge-language-modelsmulti-agent-systemspolicy-as-promptresearch-integritysimulationsocial-manipulation
Paper metadata
- arXiv ID
- 2607.11890
- Version
- Not specified by this published record
- Category
- Computer Science — Computers and Society (cs.CY)
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Evidence and limitations
- Source ID
- arxiv_cs_cy
- Record identifier
- 02312e2a27969aaeb51e5da98f1bd88b4e8f329ff9daaad1de57d0d80250ea05
- Enrichment time
- 2026-07-15T07:23:51Z
- AI-assisted enrichment
- Yes
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