Larger batch sizes for LLM dialogue coding in healthcare simulations improve speed and reduce energy consumption while decreasing coding accuracy compared to human labels.
In: Proceedings of the Twelfth ACM Conference on Learning @ Scale
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Introduces L2-Bench benchmark for AI feedback in language education across six dimensions and identifies explainability pitfalls in AI-generated explanations that appear helpful but are flawed.
citing papers explorer
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Scalable LLM-based Coding of Dialogue in Healthcare Simulation: Balancing Coding Performance, Processing Time, and Environmental Impact
Larger batch sizes for LLM dialogue coding in healthcare simulations improve speed and reduce energy consumption while decreasing coding accuracy compared to human labels.
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Ceci n'est pas une explication: Evaluating Explanation Failures as Explainability Pitfalls in Language Learning Systems
Introduces L2-Bench benchmark for AI feedback in language education across six dimensions and identifies explainability pitfalls in AI-generated explanations that appear helpful but are flawed.