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From Revolution to Ruin: An Empirical Analysis Yemen's State Collapse
Riste Ichev, Rok Spruk
https://arxiv.org/abs/2507.08512 https://

From Revolution to Ruin: An Empirical Analysis Yemen's State Collapse
We assess the broad repercussions of Yemen's 2011 revolution and subsequent civil war on its macroeconomic trajectories, human development, and quality of governance by constructing counterfactual benchmarks using a balanced panel of 37 developing countries over 1990-2022. Drawing on matrix-completion estimators with alternative shrinkage regimes and a LASSO-augmented synthetic-control method, we generate Yemen's hypothetical no-conflict paths for key macroeconomic aggregates, demographic and h…

The Revolution Has Arrived: What the Current State of Large Language Models in Education Implies for the Future
Russell Beale
https://arxiv.org/abs/2507.02180

The Revolution Has Arrived: What the Current State of Large Language Models in Education Implies for the Future
Large language Models have only been widely available since 2022 and yet in less than three years have had a significant impact on approaches to education and educational technology. Here we review the domains in which they have been used, and discuss a variety of use cases, their successes and failures. We then progress to discussing how this is changing the dynamic for learners and educators, consider the main design challenges facing LLMs if they are to become truly helpful and effective as …

This https://arxiv.org/abs/2412.16772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?
The ongoing revolution in language modeling has led to various novel applications, some of which rely on the emerging social abilities of large language models (LLMs). Already, many turn to the new cyber friends for advice during the pivotal moments of their lives and trust them with the deepest secrets, implying that accurate shaping of the LLM's personality is paramount. To this end, state-of-the-art approaches exploit a vast variety of training data, and prompt the model to adopt a particula…