AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Three attention-guided strategies are proposed: Attention-JSD, Attention-Entropy-Max, and Attention-Entropy-Min, which leverage structural information carried by internal self-attention mechanisms as a signal for layer selection and observe significant gains on multi-answer metrics.
Results demonstrate that the proposed method outperforms the baselines, including an LLM without debiasing and previous calibration methods, and it is confirmed that scoring bias varies across LLMs, tasks, and score ranges, indicating the importance of measuring latent number bias as the case may be.
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