mlsanityData to 14 Oct 2026

How Quantization Shapes Bias in Large Language Models

arXiv 2508.18088 ↗First posted 25 Aug 2025cs.CL cs.LGcited by 4

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Abstract (arXiv)

This work presents a comprehensive evaluation of how quantization affects model bias, with particular attention to its impact on individual demographic subgroups. We focus on weight and activation quantization strategies and examine their effects across a broad range of bias types, including stereotypes, fairness, toxicity, and sentiment. We employ both probability- and generated text-based metrics across 13 benchmarks and evaluate models that differ in architecture family and reasoning ability. Our findings show that quantization has a nuanced impact on bias: while it can reduce model toxicity and does not significantly impact sentiment, it tends to slightly increase stereotypes and unfairness in generative tasks, especially under aggressive compression. These trends are generally consistent across demographic categories and subgroups, and model types, although their magnitude depends on the specific setting. Overall, our results highlight the importance of carefully balancing efficiency and ethical considerations when applying quantization in practice.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 28 of its 46 references that are in the map. First posted on arXiv 25 Aug 2025.

Builds on

28 of 46 references.

References · 28

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  6. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,919
  7. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,386
  8. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,298
  9. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
  10. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 701
  11. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 659
  12. 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 518
16 more
  1. 2021BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generationcited by 412
  2. 2025MathArena: Evaluating LLMs on Uncontaminated Math Competitionscited by 297
  3. 2024Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMscited by 229
  4. 2023Don't Make Your LLM an Evaluation Benchmark Cheatercited by 194
  5. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  6. 2023Evaluating and Mitigating Discrimination in Language Model Decisionscited by 124
  7. 2023Compressing LLMs: The Truth is Rarely Pure and Never Simplecited by 62
  8. 2024Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compressioncited by 57
  9. 2023How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilitiescited by 33
  10. 2024LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkitcited by 33
  11. 2024Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compressioncited by 33
  12. 2024COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Actcited by 24
  13. 2022Can Model Compression Improve NLP Fairnesscited by 20
  14. 2023Understanding the Effect of Model Compression on Social Bias in Large Language Modelscited by 19
  15. 2024Investigating the Impact of Quantization on Adversarial Robustnesscited by 9
  16. 2024The Impact of Inference Acceleration on Bias of LLMscited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Investigating Social Bias Changes in Quantized Language Modelscited by 5
  2. 2026FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequencescited by 3
  3. 2026QuantiBias: Benchmarking Quantization-Induced Bias in LLMscited by 1
  4. 2026QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstructioncited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024You Never Know: Quantization Induces Inconsistent Biases in Vision-Language Foundation Models
  2. 2024Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency
  3. 2024Interpreting Bias in Large Language Models: A Feature-Based Approach
  4. 2026CogBias: Measuring and Mitigating Cognitive Bias in Large Language Models
  5. 2025Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective
  6. 2025Fair-GPTQ: Bias-Aware Quantization for Large Language Models
  7. 2026Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels
  8. 2025Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models