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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

The authors investigate how the quantization process used to deploy large language models in resource-limited settings can lead to unnoticed behavioral changes, even when task performance seems unaffected. They introduce a new metric called 'correctness agreement' and use it to show that these changes occur with moderate quantization levels. The authors also analyze how quantization affects attention weights and find non-linear breakpoints at low bit-widths.

What the Document Says

  • Post-training quantization is used for deploying large language models in resource-constrained settings.
  • Evaluation of these models relies mainly on accuracy and perplexity, but the authors claim these metrics fail to capture behavioral changes induced by quantization.
  • The authors introduce 'correctness agreement', a decision-level metric that measures the overlap in correct predictions between a base model and its quantized variants.
  • They find that behavioral divergence emerges under moderate quantization even when task performance appears preserved.
  • The authors analyze quantization as a structural operator on attention weights and quantify layer-wise distortions using statistical and distributional measures.
  • Non-linear breakpoints are found at low bit-widths, and query and key projections are consistently more sensitive than value and output projections.

Key Concepts Mentioned

  • post-training quantization
  • large language models
  • resource-limited settings
  • accuracy
  • perplexity
  • correctness agreement
  • behavioral divergence
  • attention weights
  • bit-widths
  • query projections
  • key projections
  • value projections
  • output projections

Claims Made by the Document

  • Evaluation of large language models relies mainly on accuracy and perplexity.
  • These metrics fail to capture behavioral changes induced by quantization.
  • Behavioral divergence emerges under moderate quantization even when task performance appears preserved.
  • Query and key projections are consistently more sensitive than value and output projections.

Evidence Used

  • The authors introduce a new metric called 'correctness agreement' to demonstrate their findings.
  • They present the results of their analysis on multiple models and quantization schemes.
  • They provide specific examples of non-linear breakpoints at low bit-widths and sensitivity differences between projections.

Methods / Approach

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Limitations Stated by the Authors

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Limitations Noted by the Agent

Agent interpretation — not from the source.

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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.