KL divergence correlates with benchmark scores over wide quantization ranges but loses all predictive power in the near-baseline silent zone because it tracks disagreement volume rather than direction.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
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Empirical evaluation of quantization effects on eight LLMs across bit widths, showing performance generally declines at lower precision but with model-size-dependent resilience and acceptable accuracy at 2 bits for many cases.
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Displacement Is Not Direction: Evaluating Fidelity Metrics for Quantized LLM Deployment
KL divergence correlates with benchmark scores over wide quantization ranges but loses all predictive power in the near-baseline silent zone because it tracks disagreement volume rather than direction.
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K-Quantization and its Impact on Output Performance
Empirical evaluation of quantization effects on eight LLMs across bit widths, showing performance generally declines at lower precision but with model-size-dependent resilience and acceptable accuracy at 2 bits for many cases.
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