arXiv subjects

Isabella Cruz

Publications and source records attributed to Isabella Cruz.

2 recordsLinked to original sources

arXiv2026

Helping People Choose Careers in the Age of AI

How should people choose careers when artificial intelligence (AI) is rapidly transforming the nature of work? We first compare six recent projections of occupational exposure to task automation with AI, examining their methods and assumptions. We then propose a new empirical model of occupational AI exposure based on 2025 query data from Anthropic and OpenAI. We find marked heterogeneity in model predictions, though models published since 2020 show positive relationships among AI exposure, salaries, and occupational complexity. To reduce uncertainty due to heterogeneous assumptions about task automation potential, we average the projections from five models, including our own. Using these averages, we report on likely tradeoffs between salaries and AI exposure across interest categories, O*NET Job Zones, and job fields. Jobs in healthcare practice show the strongest balance of higher pay with lower AI exposure. Among jobs making high use of Anthropic's Claude, those that use it as a complement rather than a substitute for human work are modestly higher-paying, though whether this pattern holds will depend on usage norms adopted in each field.

econ.GN↗

arXiv2024

Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization

Large language models (LLMs) have revolutionized how we interact with technology, but their personalization to individual user preferences remains a significant challenge, particularly in on-device applications. Traditional methods often depend heavily on labeled datasets and can be resource-intensive. To address these issues, we present Adaptive Self-Supervised Learning Strategies (ASLS), which utilizes self-supervised learning techniques to personalize LLMs dynamically. The framework comprises a user profiling layer for collecting interaction data and a neural adaptation layer for real-time model fine-tuning. This innovative approach enables continuous learning from user feedback, allowing the model to generate responses that align closely with user-specific contexts. The adaptive mechanisms of ASLS minimize computational demands and enhance personalization efficiency. Experimental results across various user scenarios illustrate the superior performance of ASLS in boosting user engagement and satisfaction, highlighting its potential to redefine LLMs as highly responsive and context-aware systems on-device.

cs.CL↗