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Professor of economics at George Mason University and Bartley J. Madden Chair at the Mercatus Center. Co-writes the blog Marginal Revolution and co-authors the textbook Modern Principles of Economics with Tyler Cowen.

• Our readingkorrents.com

  Under binding ceilings, cost-minimizing suppliers drive allocations to corners rather than Harberger or random benchmarks, producing larger losses and discontinuous welfare jumps.

  benchmarks

  Their words

  We show that equilibrium generically occurs at neither the Harberger nor Glaeser-Luttmer benchmark. Cost-minimizing suppliers drive allocations to vertices, not interiors. Corners are not an assumption but an outcome about what cost-minimizing suppliers choose. The correct benchmark is corners, not random, and corners generate qualitatively different welfare properties: losses far larger than either efficient or random distributions, and discontinuous jumps from small parameter perturbations.

  arxiv.org

Senior economist at the Foundation for American Innovation; writes Second Best, and was the Niskanen Center's director of social policy before that.

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  Text embeddings produced by different large language models converge on a shared, universal geometric structure regardless of architecture or training data.

  LLMs

  Their words

  the text embeddings across LLMs appear to largely converge on a "universal geometry" despite differing architectures, parameter counts, and training sets.

  Hegel and the AI Mindsecondbest.ca

Research scientist at Meta in Berlin working on multilingual models and evaluation; led the multilingual team at Cohere and was a research scientist at Google DeepMind before that.

• Our readingkorrents.com

  Full fine-tuning of large language models has become too costly, making parameter-efficient fine-tuning methods necessary.

  LLMs

  Their words

  Full fine-tuning of LLMs has become prohibitive and requires parameter-efficient methods instead.

  EMNLP 2023 Primerruder.io