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cs.CL2026
Who Do Language Models Think Is Competent? A Mechanistic Analysis of Occupational Bias
Keren Fuentes, Aaron Mueller
Language models (LMs) often pass behavioral bias evaluations, but it remains unclear whether they no longer represent the underlying associations that give rise to biases, or have…
cs.CL2024
Inverse Scaling: When Bigger Isn't Better
Ian R. McKenzie, Alexander Lyzhov, Michael Pieler +24
Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, w…
cs.CL2024
In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax
Aaron Mueller, Albert Webson, Jackson Petty +1
In-context learning (ICL) is now a common method for teaching large language models (LLMs) new tasks: given labeled examples in the input context, the LLM learns to perform the tas…