Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

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

Source

arXiv

arxiv.org/abs/2607.08734v1

Read the source

Source

Plain-Language Brief

The authors of this paper introduce a new method to evaluate the effects of post-training quantization on large language models, as this technique is commonly used to deploy models in resource-limited settings. The research shows that using accuracy and perplexity as evaluation metrics can lead to an 'illusion of equivalency' between base and quantized models, as these metrics do not capture behavioral changes caused by quantization. Instead, they propose a new metric called correctness agreement which measures the overlap in correct predictions between a base model and its quantized variants.

What the Document Says

  • Post-training quantization is used to deploy large language models in resource-constrained settings.
  • Evaluation of these models relies on accuracy and perplexity, but these metrics do not capture behavioral changes induced by quantization.
  • The authors introduce correctness agreement, a metric that measures the overlap in correct predictions between a base model and its quantized variants.
  • Behavioral divergence emerges under moderate quantization even when task performance appears preserved.
  • Quantization is analyzed as a structural operator on attention weights.
  • Layer-wise distortions are quantified using statistical and distributional measures.
  • Non-linear breakpoints at low bit-widths were found.
  • Query and key projections are consistently more sensitive than value and output projections.
  • The findings expose an illusion of equivalence between base and quantized models.
  • Behavioral evaluation beyond conventional performance metrics is motivated.

Key Concepts Mentioned

  • post-training quantization
  • large language models
  • resource-constrained settings
  • accuracy
  • perplexity
  • correctness agreement
  • attention weights
  • bit-widths
  • query and key projections
  • illusion of equivalence

Claims Made by the Document

  • Post-training quantization evaluation relies on inadequate metrics.
  • Correctness agreement can capture behavioral changes induced by quantization.
  • Behavioral divergence emerges under moderate quantization.
  • Quantization causes non-linear breakpoints at low bit-widths.
  • Query and key projections are more sensitive to quantization than value and output projections.

Evidence Used

  • The authors show that behavioral changes emerge even when task performance appears preserved.
  • Statistical and distributional measures are used to quantify layer-wise distortions caused by quantization.
  • Findings reveal non-linear breakpoints at low bit-widths and consistently more sensitive query and key projections.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.