When Quantization Affects Confidence of Large Language Models? (2024.findings-naacl)
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| Challenge: | Existing studies have shown that quantization compromises performance and exacerbates biases in Large Language Models. |
| Approach: | They propose an explanation for quantization loss based on confidence levels . they propose a range of efficient compression and acceleration methods including quan-tization . |
| Outcome: | The proposed methods show that quantization decreases confidence regarding true labels and that it exacerbates biases across different scales. |
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A Comprehensive Evaluation of Quantization Strategies for Large Language Models (2024.findings-acl)
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| Challenge: | Quantization studies have focused on instruction-tuned LLMs, leaving their performance on other benchmarks unclear. |
| Approach: | They propose a framework to evaluate quantized large language models using four dimensions . they propose to reduce the bits needed for model weights or activations with minimal performance loss . |
| Outcome: | The proposed framework can retain comparable performance to non-quantized LLMs on most benchmarks. |
Quantized Can Still Be Calibrated: A Unified Framework to Calibration in Quantized Large Language Models (2025.acl-long)
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| Challenge: | Existing methods to quantify uncertainty of large language models (LLMs) but their influence on uncertainty calibration remains unexplored. |
| Approach: | They propose an analytic method to estimate the upper bound of calibration error (UBCE) for quantized LLMs and propose a method to recover calibration errors through soft-prompt tuning. |
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On the Impact of Calibration Data in Post-training Quantization and Pruning (2024.acl-long)
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| Challenge: | Quantization and pruning are the foundations of compression for large language models . however, no prior work has investigated how calibration data impacts performance of compression methods. |
| Approach: | They propose an empirical study on the effect of calibration data on LLM performance. |
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Does quantization affect models’ performance on long-context tasks? (2025.emnlp-main)
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| Challenge: | Large language models support context windows exceeding 128K tokens, but this comes with significant memory requirements and high inference latency. |
| Approach: | They present the first systematic evaluation of quantized LLMs on tasks with long inputs and long-form outputs. |
| Outcome: | The proposed method preserves accuracy, while 4-bit methods lead to substantial losses . the results highlight the importance of a careful evaluation before deploying quantized LLMs . |
Why Do Some Inputs Break Low-Bit LLM Quantization? (2025.emnlp-main)
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| Challenge: | Low-bit weight-only quantization reduces memory usage but disproportionately affects certain examples. |
| Approach: | They analyze quantization errors of 50 pairs of methods on large language models and test their hypothesis . |
| Outcome: | The proposed method reduces the memory footprint of large language models while maintaining reasonable performance across benchmarks. |
How Does Quantization Affect Multilingual LLMs? (2024.findings-emnlp)
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Kelly Marchisio, Saurabh Dash, Hongyu Chen, Dennis Aumiller, Ahmet Üstün, Sara Hooker, Sebastian Ruder
| Challenge: | Quantization is widely used to improve inference speed and deployment of large language models. |
| Approach: | They conduct a thorough analysis of quantized multilingual LLMs . they find language disparately affected by quantization, non-Latin script languages worst . authors urge consideration of multilingual performance as evaluation criterion for efficient models . |
| Outcome: | The results show that quantization has harmful effects on human evaluation . language performance is disparately affected by quantization, the authors say . |
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) require significant computational resources for deployment and use. |
| Approach: | They propose to use low-bit quantization methods to reduce memory footprint and increase inference rate to improve performance of Large Language Models. |
| Outcome: | The proposed methods can reduce the memory footprint and increase the inference rate of LLMs. |
Understanding the Effect of Model Compression on Social Bias in Large Language Models (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) trained with self-supervision on vast corpora of web text fit to the social biases of that text, leading to representational harm. |
| Approach: | They propose to use quantization and knowledge distillation to reduce the computational burden of LLMs to mitigate the effects of inappropriate social biases learned during pretraining. |
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When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) exhibit excellent performance in various tasks, but memory requirements present a challenge when deploying on memory-limited devices. |
| Approach: | They propose a framework to compress LLM after quantization further, achieving about 2.2x compression ratio. |
| Outcome: | The proposed model can achieve 40% reduction in memory size with negligible loss in accuracy and inference speed. |
Can Post-Training Quantization Benefit from an Additional QLoRA Integration? (2025.naacl-industry)
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| Challenge: | Large language models require considerable computing resources, which can be costly and often unavailable. |
| Approach: | They propose to integrate 4-bit Post-training Quantization with QLoRA to address these issues . they demonstrate that the integration outperforms standard quantization and fine-tuning . |
| Outcome: | The proposed integration outperforms standard PTQ and 16-bit full-parameter fine-tuning on LLMs. |