arXiv:2607.08734v1 Announce Type: new Abstract: Post-training quantization is widely used to deploy large language models in resource-constrained settings, yet its evaluation relies almost exclusively on accuracy and perplexity. We show that these metrics fail to capture behavioral changes induced by quantization. We introduce correctness agreement, a decision-level metric that measures overlap in correct predictions between a base model and its quantized variants, independent of absolute accuracy. Across multiple models and quantization schemes from 8-bit to 2-bit, we find that behavioral div
The increasing push for LLM deployment in resource-constrained environments necessitates more nuanced evaluation methods for quantization effects beyond simple accuracy scores.
This research highlights that current quantization evaluation metrics are insufficient, potentially leading to misjudgments about LLM reliability and performance in real-world applications.
The focus for evaluating quantized LLMs will shift from solely accuracy and perplexity to include decision-level metrics like correctness agreement, revealing behavioral changes not previously captured.
- · Developers of robust quantized LLMs
- · Users of resource-constrained AI applications
- · Quantization research firms
- · LLM developers relying solely on traditional metrics
- · Benchmarking organizations with limited evaluation frameworks
Quantized LLMs will become more trustworthy for sensitive applications, as their behavioral fidelity is better understood.
New quantization techniques prioritizing behavioral consistency over raw accuracy will likely emerge and gain prominence.
The definition of 'good enough' for LLM deployment will evolve, incorporating decision-level robustness alongside traditional performance metrics.
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Read at arXiv cs.AI