The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
Researchers propose a decision-level metric called correctness agreement to evaluate quantized LLMs, showing that standard accuracy metrics miss behavioral changes.
- Correctness agreement measures overlap of correct outputs, revealing changes hidden from accuracy metrics.
- Quantized LLMs can exhibit significant behavioral shifts even with modest bit reductions.
- Standard evaluation methods may underestimate the impact of model compression on downstream tasks.
The paper addresses a gap in how post‑training quantization of large language models is measured. While accuracy and perplexity are commonly reported, they do not capture subtle changes in model behavior.
The authors define a new metric, correctness agreement, which compares the overlap of correct predictions between the original model and its quantized versions, independent of overall accuracy. This decision‑level approach highlights differences that traditional metrics overlook.
Experiments across several LLM architectures and quantization schemes ranging from 8‑bit down to 2‑bit demonstrate that even moderate quantization can cause noticeable behavioral divergence, despite minimal drops in accuracy scores.
The findings suggest that developers and researchers need more nuanced evaluation tools when compressing models for edge devices or cost‑constrained deployments.
Provides a better tool to assess quantized models before deployment.
Helps avoid costly performance surprises when compressing AI services.
Highlights the importance of robust evaluation in model‑efficiency investments.
Introduces a novel research direction on model evaluation and compression.
- correctness agreement
- A metric that quantifies the overlap of correct predictions between two models, independent of overall accuracy.
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