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Source note2 min read

The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs

Source

arXiv

arxiv.org/abs/2607.08734v1

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Plain-Language Brief

This research study introduces a new metric, called 'correctness agreement', to evaluate the impact of post-training quantization on large language models. The authors show that even when task performance seems unaffected by moderate quantization, there are behavioral changes in the models. They analyze these changes using statistical and distributional measures and find that certain projections within the model are more sensitive to quantization than others.

What the Document Says

  • The research focuses on post-training quantization of large language models for resource-constrained settings.
  • Evaluation of these quantized models primarily relies on accuracy and perplexity.
  • However, the authors argue that these metrics do not capture behavioral changes induced by quantization.
  • They propose a new metric, 'correctness agreement', which measures the overlap in correct predictions between a base model and its quantized variants, independent of absolute accuracy.
  • The study finds behavioral divergence under moderate quantization even when task performance appears preserved.
  • This effect is analyzed as a structural operator on attention weights.
  • Statistical and distributional measures are used to quantify layer-wise distortions in the model.
  • Non-linear breakpoints at low bit-widths and consistent sensitivity of query and key projections are revealed.
  • The findings suggest an illusion of equivalence between base and quantized models and highlight the need for behavioral evaluation beyond conventional performance metrics.

Key Concepts Mentioned

  • post-training quantization
  • large language models
  • resource-constrained settings
  • accuracy
  • perplexity
  • correctness agreement
  • attention weights
  • statistical measures
  • distributional measures
  • breakpoints
  • projections

Claims Made by the Document

  • Existing evaluation metrics for post-trained quantized models are inadequate.
  • Behavioural changes occur under moderate quantization even when task performance appears preserved.
  • Quantization can be analyzed as a structural operator on attention weights.
  • Non-linear breakpoints exist at low bit-widths.
  • Query and key projections are consistently more sensitive than value and output projections.

Evidence Used

  • The authors demonstrate the inadequacy of existing evaluation metrics through their findings.
  • Behavioral changes are observed and quantified through statistical and distributional measures.
  • Non-linear breakpoints and sensitivity of certain projections are revealed through analysis.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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 divergence emerges under moderate quantization even when task performance appears preserved. To explain this effect, we analyze quantization as a structural operator on attention weights and quantify layer-wise distortions using statistical and distributional measures. Our results reveal non-linear breakpoints at low bit-widths and show that query and key projections are consistently more sensitive than value and output projections. These findings expose an illusion of equivalence between base and quantized models and motivate behavioral evaluation beyond conventional performance metrics.