AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Debias-SparseGPT is introduced, a post-training pruning method incorporating representational debiasing using a second-order term defined over demographically contrasting inputs that consistently reduces pruning-induced bias compared to SparseGPT while preserving model perplexity and zero-shot accuracy.
It is found that instruction tuning consistently increases answer confidence, despite limited changes in predictive accuracy, while degrading likelihood-based calibration, and a non-uniform effect of instruction tuning on rationale diversity is observed.
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