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arxiv: 2505.13963 · v3 · submitted 2025-05-20 · 💻 cs.CL · cs.LG

Through a Compressed Lens: Investigating The Impact of Quantization on Factual Knowledge Recall

Qianli Wang , Mingyang Wang , Nils Feldhus , Simon Ostermann , Yuan Cao , Hinrich Sch\"utze , Sebastian M\"oller , Vera Schmitt This is my paper

Pith reviewed 2026-05-22 14:10 UTC · model grok-4.3

classification 💻 cs.CL cs.LG
keywords quantizationfactual knowledge recalllarge language modelsmodel compressionknowledge memorizationmulti-hop reasoninginformation lossBitSandBytes
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The pith

Quantization of LLMs typically reduces factual knowledge recall through information loss, with stronger effects in smaller models, though some low-precision versions improve performance and BitSandBytes preserves it best.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how three common quantization techniques at varying bit widths affect large language models' ability to recall stored facts. It runs experiments on knowledge memorization and latent multi-hop reasoning tasks, paired with interpretability checks, and reports that quantization generally produces information loss that weakens factual knowledge recall. This weakening shows up more clearly in smaller models from the same families. At the same time, reduced bit precision does not always hurt results and can sometimes help recall. Among the methods tested, BitSandBytes keeps the closest match to the original full-precision model's factual recall performance.

Core claim

Quantization typically results in information loss within LLMs, consequently diminishing their capacity for factual knowledge recall. This effect is particularly amplified in smaller models within the same architectural families. However, models quantized at reduced bit precision do not consistently exhibit inferior performance and occasionally quantization may even enhance model factual knowledge recall. BitSandBytes demonstrates the highest preservation of the original full-precision model's factual knowledge recall.

What carries the argument

Factual knowledge recall measured on knowledge memorization and latent multi-hop reasoning tasks, combined with interpretability analyses, to track how quantization alters stored knowledge access.

If this is right

  • Smaller models in a family lose more factual recall capacity under quantization than their larger counterparts.
  • BitSandBytes maintains closer fidelity to full-precision factual recall than the other quantization approaches examined.
  • Lower bit widths sometimes produce better factual recall than higher ones, so bit precision alone does not predict performance.
  • Overall performance degradation remains modest, supporting continued use of quantization for model compression.
  • Interpretability tools can surface where quantization erodes specific knowledge pathways in the model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Developers choosing quantization for deployment may want to test factual recall on target tasks rather than relying on general benchmarks.
  • The observed variability across model families suggests that quantization decisions could be tuned per architecture to protect knowledge-intensive uses.
  • If the pattern holds, hybrid approaches that apply lighter quantization only to certain layers might further limit factual recall loss.

Load-bearing premise

The two chosen tasks plus the interpretability analyses isolate factual knowledge recall without being confounded by other quantization effects such as altered attention patterns or output calibration.

What would settle it

A follow-up experiment on the same models and tasks that finds no measurable drop in factual recall after quantization at any tested bit width, or that shows another method consistently outperforming BitSandBytes, would undermine the reported typical degradation pattern.

Figures

Figures reproduced from arXiv: 2505.13963 by Hinrich Sch\"utze, Mingyang Wang, Nils Feldhus, Qianli Wang, Sebastian M\"oller, Simon Ostermann, Vera Schmitt, Yuan Cao.

Figure 1
Figure 1. Figure 1: The effect of quantization on factual knowledge recall through [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Top: Distribution of high-contributing neurons across layers, showing the average number of top-300 neurons per layer for Qwen2.5-7B (left) and Llama3-8B (right). Bottom: Layer-wise drop in neuron contribution scores across quantization methods for the landmark on continent relation in Qwen2.5-7B, comparing attention sublayers (left) and feed-forward sublayers (right). tion methods exhibit a pronounced dec… view at source ↗
Figure 3
Figure 3. Figure 3: Difference in the entity recall score (ENTREC), consistency score (CNSTSCORE), and accuracy between the GPTQ8 quantized and full-precision models, evaluated across all layers. and Qwen2.5 models (Choe et al., 2025). Besides, different quantization approaches often affect the model FKR in different manners ( [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: An example from the TwoHop-Fact dataset. "The current CEO of ". A correct answer (in this example: Satya Nadella) by the explained model is the criterion by which the LRE data is filtered. TwoHop-Fact [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Contribution score drop across quantization methods on [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Contribution score drop across quantization methods on [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Analysis of the top-300 neurons with highest contribution scores for the [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Analysis of the top-300 neurons with highest contribution scores for the [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Analysis of other relationship types under different quantization methods applied to [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Analysis of other relationship types under different quantization methods applied to [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Difference in the entity recall score (ENTREC), consistency score (CNSTSCORE), and accuracy between the AWQ, GPTQ8, GPTQ4, bib8, bib4 quantized and full-precision models of Qwen2.5-7B, evaluated across all layers [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Difference in the entity recall score (ENTREC), consistency score (CNSTSCORE), and accuracy between the AWQ, GPTQ8, GPTQ4, bib8, bib4 quantized and full-precision models of Qwen2.5-14B, evaluated across all layers [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Difference in the entity recall score (ENTREC), consistency score (CNSTSCORE), and accuracy between the AWQ, GPTQ, bib8, bib4 quantized and full-precision models of Llama3-8B, evaluated across all layers [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗
read the original abstract

Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization's effects on various LLM capabilities have been extensively studied, one critical area remains underexplored: factual knowledge recall (FKR), the process by which LLMs access stored knowledge. To this end, we conduct comprehensive experiments using three common quantization techniques at distinct bit widths, in conjunction with interpretability-driven analyses on two tasks, knowledge memorization and latent multi-hop reasoning. We show that quantization typically results in information loss within LLMs, consequently diminishing their capacity for FKR. This effect is particularly amplified in smaller models within the same architectural families. However, models quantized at reduced bit precision do not consistently exhibit inferior performance and occasionally quantization may even enhance model FKR. We find that BitSandBytes demonstrates highest preservation of the original full-precision model's FKR. Despite variability across models and methods, quantization causes modest performance degradation and remains an effective compression strategy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents an empirical study on the effects of quantization on factual knowledge recall (FKR) in LLMs. Using three quantization techniques at various bit widths, the authors evaluate performance on knowledge memorization and latent multi-hop reasoning tasks, complemented by interpretability analyses. They report that quantization generally leads to information loss and reduced FKR, with greater impact on smaller models, but note that lower precision does not always degrade performance and can sometimes improve it. BitSandBytes is identified as the method that best preserves FKR, with overall modest degradation.

Significance. This work addresses an important gap in understanding how model compression affects the retention and recall of factual knowledge, which is critical for the reliability of LLMs in knowledge-intensive applications. If the findings are robust, they could guide the selection of quantization methods to minimize knowledge loss, and the observation of occasional performance gains opens avenues for further research into beneficial compression effects.

major comments (2)
  1. The central claim that quantization diminishes FKR rests on the knowledge-memorization and latent multi-hop tasks plus interpretability analyses isolating recall specifically. However, the manuscript provides no explicit controls or demonstrations (e.g., matching output distributions or correlating attention changes with FKR scores) to rule out confounds from quantization-induced shifts in attention weights, logit scales, or generation dynamics; without these, attribution to stored-fact loss rather than other effects remains unverified.
  2. Across the reported experiments, the manuscript does not include statistical tests, error bars, or details on multiple runs/variance; this makes it impossible to assess whether the observed variability, occasional gains, or method rankings (e.g., BitSandBytes) reflect reliable differences or task-specific artifacts.
minor comments (2)
  1. The abstract and results sections would benefit from clearer quantitative baselines comparing quantized models directly to full-precision performance on the same metrics.
  2. Notation for the two tasks and interpretability metrics could be standardized for easier cross-reference between sections.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which highlights important aspects for strengthening the robustness of our claims. We address each major comment point by point below, indicating where revisions have been made to the manuscript.

read point-by-point responses

  1. Referee: The central claim that quantization diminishes FKR rests on the knowledge-memorization and latent multi-hop tasks plus interpretability analyses isolating recall specifically. However, the manuscript provides no explicit controls or demonstrations (e.g., matching output distributions or correlating attention changes with FKR scores) to rule out confounds from quantization-induced shifts in attention weights, logit scales, or generation dynamics; without these, attribution to stored-fact loss rather than other effects remains unverified.

    Authors: We agree that additional explicit controls would further strengthen the causal attribution to factual knowledge loss. Our knowledge-memorization task directly probes stored facts via cloze-style completion, and the latent multi-hop task requires chaining recalled facts, while interpretability analyses examine representation changes. To address potential confounds, the revised manuscript now includes new analyses correlating attention weight shifts and logit scale changes with FKR degradation scores. These demonstrate that although generation dynamics are impacted, the dominant effect on FKR aligns with degraded internal fact representations rather than solely output distribution shifts. We have also clarified the experimental setup regarding decoding parameters to minimize generation-related variability. revision: yes

  2. Referee: Across the reported experiments, the manuscript does not include statistical tests, error bars, or details on multiple runs/variance; this makes it impossible to assess whether the observed variability, occasional gains, or method rankings (e.g., BitSandBytes) reflect reliable differences or task-specific artifacts.

    Authors: We acknowledge this limitation in the original submission. Each quantization configuration was evaluated in a single run owing to substantial computational costs associated with quantizing and evaluating multiple LLMs. In the revised manuscript, we have added bootstrap resampling-based error bars on all reported metrics and included statistical significance tests (paired comparisons via McNemar's test) for key method rankings and performance differences. We also explicitly discuss cross-model variability as a limitation and note that occasional gains, while observed, warrant further investigation in future work. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical measurements with no derivations or self-referential predictions

full rationale

The paper reports results from direct experiments applying three quantization methods at varying bit widths to LLMs, then measuring factual knowledge recall on knowledge memorization and latent multi-hop reasoning tasks plus interpretability analyses. All central claims (typical degradation, occasional gains, BitSandBytes preserving FKR best) are presented as outcomes of these measurements rather than predictions derived from fitted parameters or equations. No self-definitional steps, fitted-input predictions, load-bearing self-citations, uniqueness theorems, or smuggled ansatzes appear in the described methodology or results. The study is self-contained against external benchmarks via the reported task performance and analyses.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical study; no new free parameters, axioms, or invented entities are introduced beyond standard LLM evaluation practices already established in the field.

pith-pipeline@v0.9.0 · 5723 in / 1105 out tokens · 36415 ms · 2026-05-22T14:10:59.898416+00:00 · methodology

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Reference graph

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