The authors propose a retrieval-augmented framework that grounds AI exposure labels for 18,796 O*NET occupation-task pairs in retrieved news and academic abstracts, outperforming zero-shot prompting in 72% of disagreements and aligning better with observed real-world usage.
Chain-of-thought prompting elicits reasoning in large language models.Advances in Neural Information Processing Systems, 35:24824–24837
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A self-healing LLM pipeline for natural language to PostgreSQL translation achieves up to 9.3 percentage point accuracy gains on benchmarks through error diagnosis and anti-regression mechanisms.
A persona-driven multi-agent framework with a three-dimensional decision-theoretic evaluation shows that agent-persona alignment significantly impacts performance and coordination in O-RAN optimization challenges.
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Jobs' AI Exposure Should Be Measured from Evidence, Not Model Priors
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