Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models
Large Language Models;Quantization;Safety Alignment
Yes
No ethics review needed.
* It would be useful to study the sensitivity of ASR results with the temperature and observe if the deterioration after quantization is worse or more gradual at higher temperatures.

* "the ASR rises from 29.80% on the pre-quantization Llama-2-7b-chat model to 42.40% on the INT4 model and to 39.10% on the INT8 model. Similarly, for the Gemma-7b-instruct model, the ASR increases from 9.40% pre-quantization to 17.90% on the INT4 model and to 15.10%" - it appears that the decline is more steep going to INT8 compared to going to INT4. It would be good to add discussion on this and conduct more experiments to understand this phenomenon and see if there is any saturation.
+ Comprehensiveness: The study covers all four mainstream categories of LLM quantization techniques covering two post-quantization techniques and two quantization-aware training/finetuning techniques. For quantization techniques needing additional quantization-assisting dataset, the paper uses three datasets with varying safety risk levels: a directly harmful dataset, an indirectly harmful dataset, and a benign dataset. The quantized LLMs are evaluated with two commonly adopted bit-widths.

+  The paper also looks at the feasibility of identifying and updating a small portion of safety-critical weights, then exploit potential tools for identifying these weights, and construct a pair of masking matrices corresponding to the LoRA variables. This is clearly a novel contribution.
The paper studies the extent to which the different quantization techniques degrade the safety capabilities of quantized LLMs, and how can such declines in safety capabilities be mitigated.
The paper makes a claim that "Although preliminary literature reports scattered evidence of safety evaluations for quantized LLMs, they are not systematic enough to support a well-rounded evaluation". Its primarily claim to novelty appears to be that it is the first to study the safety impact of quantized LLMs. Here is a list of work studying different aspects of quantizations impact on safety and alignment:

- Belkhiter, Yannis, Giulio Zizzo, and Sergio Maffeis. "HarmLevelBench: Evaluating Harm-Level Compliance and the Impact of Quantization on Model Alignment." Neurips Safe Generative AI Workshop 2024.

- Egashira, Kazuki, Mark Vero, Robin Staab, Jingxuan He, and Martin Vechev. "Exploiting LLM Quantization." arXiv preprint arXiv:2405.18137 (2024).

- Belkhiter, Yannis, Giulio Zizzo, and Sergio Maffeis. "HarmLevelBench: Evaluating Harm-Level Compliance and the Impact of Quantization on Model Alignment." In Neurips Safe Generative AI Workshop 2024.

- Hong, Junyuan, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer et al. "Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression." arXiv preprint arXiv:2403.15447 (2024).

The claim that the paper is the "first systematic safety risk assessment of quantization on LLMs and mitigate the safety degradation" is too bold and not fully substantiated. It also appears unneeded. The reviewer will raise the score if the claim is better substantiated or moderated. Letting a paper be published with such a bold not-fully-substantiated claim is not feasible as it could confuse the community.
Yes
No ethics review needed.
Please answer the points raised in the “Weakness” section.
The paper studies an important but relatively underexplored problem. The evaluation of existing quantization approaches clearly demonstrates the safety issues of quantization and Q-resafe gives significant benefits.
This paper studies the safety risks of quantization for LLMs. It first presents a detailed evaluation on how quantization reduces LLM’s safety, covering four quantization methods, three types of quantization-assisting datasets, and two bit-widths. To address the safety issue of quantization, the paper proposes Q-resafe, an approach that fine-tunes the safety-related weights of quantized LLMs to restore safety. The experiments demonstrate the consistent benefits of Q-resafe across the board.
- The paper claims to be the first systematic assessment of safety risks of quantization. However, I am aware of at least two prior papers in this direction [1, 2]. I suggest the authors conduct a more comprehensive literature review and adjust the claim.

- While the paper studies more advanced quantization algorithms, it does not cover algorithms that are already popular, such as LLM.int8(), NF4, and FP4, implemented in the bitsandbytes library. The safety issues of these popular algorithms could have a larger impact on the users.

- The paper sometimes makes unsubstantiated claims. At lines 204-209, the paper says “Following the established practice in literature” but does not provide any reference. I am also confused at this point: why would users like to use direct harmful datasets or indirectly harmful datasets for assisting quantization? Moreover, at line 413, the paper writes “As recent studies have observed” without giving any citations. I believe identifying safety-critical weights is itself a research challenge. How does Q-resafe’s approach generalize?

- Why do the authors adopt a post-hoc fixing approach for Q-resafe? Have the authors considered incorporating safety mechanisms already during quantization?

- The writing needs some adjustments. First, line 123 is an unfinished sentence. Second, the baseline numbers for the quantization methods are not clearly presented. Table 2 says 0.3 and 9.2, but line 256 suddenly says 29.8 and 9.4, which are not mentioned before.

- On page 11, the paper has a “Limitations” section. I am not sure if this is the correct place to discuss limitations. It may not align with the ICLR formatting requirements.

[1] Hong, Junyuan, et al. "Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression." ICML 2024.

[2] Egashira, Kazuki, et al. "Exploiting LLM Quantization." NeurIPS 2024.
Yes
No ethics review needed.
- Can we collect the safety-related preference dataset in Q-resafe for offline use? For example, to substantiate an auxiliary loss to guide the safety-in-mind QAT.
- The design of locating safety-critical weights assumes that the safety ability of LLMs can located in a limited pool of weights. It is unclear whether they exhibit a positive or negative relationship with safety? If positive, what about sustaining the precision of the safety-critical weights and only quantizing other weights? From a safety-at-first perspective, how strong safety performance can such a method ensure? 
- Safety is a vague concept. Without data, how to identify the security-critical weights? Are detailed descriptions missing?
- Is the safety ability of the quantized LLM always bottlenecked by its full-precision counterpart?
1. This paper raises an intriguing research question: how does quantization impact LLM safety performance? To answer this, the work makes a comprehensive measurement across different quantization settings.

2. The proposed Q-resafe method is effective. It can almost perfectly sustain the safety ability of quantized LLM in the studied safety evaluation.
With weights quantized into smaller bit-widths, LLMs are losing performance in utility. This paper observes LLM quantization from a safety perspective and conducts a measurement study concerning the impacts of quantization on LLM safety performance. The measurement study considers varying quantization bit-widths, different quantization strategies, and different settings of quantization-assisting datasets. It reveals that quantization can significantly comprise the safety ability of the quantized LLMs.

To mitigate the safety degradation, this work proposes to patch the quantized LLM in a similar fashion to the LoRA adapter. They construct a safety-related preference dataset by collecting the responses from the full-precision LLM and the quantized LLM. Employing the preference pairs, they use the DPO loss to implement a safety alignment. Concretely, they periodically locate safety-critical weights and focusing updating them to restore the safety ability. The experiments cover three preference dataset settings and yield positive results with the Q-resafe framework.
1. **Intriguing but less novel topic.** For the safety risks coming with quantization, there have been a great number of works revealing it, from the vision field (e.g., https://dl.acm.org/doi/10.1145/3485832.3485881) to recent LLM research (e.g., https://arxiv.org/abs/2405.18137). From another perspective, safety performance is just one intrinsic ability of LLM. The finding of safety degradation after quantization is not impressive. 
2. **Writing is not self-inclusive.** The four considered quantization methods are essential for understanding why they behave differently. The lack of technical details of the surveyed methods hinders understanding the rationales behind the security risks incurred by quantization.
3. **Lack of ablation study.** I have not noticed why the operation of safety-critical weight identification is necessary. And no ablation study provides evidence for employing it.
4. **Lack of impressive insights**: To be honest, I think the analysis for the measurement results is not in-depth. More discussions about safety benchmarks and utility benchmarks are in demand. Although it looks scary in Table 3 that the safety performance is almost compromised after quantization, utility and safety performance may be non-equally scaled.

Minor mistakes:
1. Mistaken references are observed in line 190 and line 402.
2. The symbol $Q_0$ in Eq. 2 I think should be $Q^0$.
3. In line 413, `As recent studies have observed, LLMs exhibit localization properties.` What recent studies it refers to?
4. Please use the same title in the paper and the website submission.
Yes
No ethic problems found.
No ethics review needed.
Thanks for submitting the paper. I agree with the motivation of the paper that quantization needs to preserve safety enforcement as well, which is not explicitly analyzed in existing work. The paper made a contribution in this domain. However, problems in experiment setups and technical contributions make me unsure if the current version is ready to publish.

First of all, I am convined that safety is affected after quantization given the empirical analysis Section 3. Based on the phenomenon, I am curious if the authors have hypothesis or insights why safety is affected more than utility. Also, the experiments of safety assessment are confusing in some places:

The metrics are critical for understanding multiple tables but details are placed in Appenfix Table 5. For instance, Table 3 says the safety metric is $ASR_{Vanilla}$ but according to Table 5, the safety metric corresponding to quantization with Indirect Harmful Datasets should be $ASR_{AoA}$, and for AWQ the metric should be $ASR_{Decoding}$. Please be precise in using the notations.

Quantization datasets and the dataset for safety assessment all rely on AdvBench. Is there potential risk of overlap of training/fine-tuning and testing data?

In Table 5, the harmful datasets for quantization sounds quite small. As far as I see, fine-tuning of LLM quantification requires large amount of data, such as hundreds of gigabytes in the original LLM-QAT work. Also, in terms of harmful datasets for quantization, do you mean adding additional harmful data to benign datasets, or do quantization with completely harmful datasets? It does not sound realistic if it is the later. Also, what if involving safety-enforced conversations in the datasets for quantization?

Second, intuitively I think Q-safe, the safety patching method after quantization is valid in its design. However, the experiments lack a comprehensive analysis of the characteristic of the method. I have following questions:

What are the baselines, or ablation study? Baselines in my mind include: (1) directly using dataset for safety fine-tuning during the quantization; (2) directly using dataset for safety fine-tuning when using Q-safe, to show the benefit of the safety-patching dataset construction; (3) do simple LoRA fine-tuning when using Q-safe, to show the benefit of periodic safety-critical weights identification.

What is the technical contribution comparing with methods of safety alignment on LLMs, e.g., instruction tuning with safety-aligned data adopted by Llama (Llama: Open and efficient foundation language models)? I raised this question because the design of Q-safe looks independent with quantization and can work on any LLMs.

Minor issues:
* Section 4.1 Row 229, it is Table 5 not Appendix 5.
* Abbreviation DPO is used at the first time in Introduction, row 112, without explanation.
* The problem the paper looks at is interesting. Quantization methods indead need to ensure safety alignment as well.
* The experiment reveals an interesting phenomenon that quantization damages safety more than utility.
The paper aims to assess the affect on safety alignment of quantization methods, and propose a safety patching method, Q-safe to improve the safety alignment of quantized models. The paper reveals a phenomenon that safety alignment is more damaged than general utility after quantization, considered different models and quantization methods. Q-safe mitigates this degradation by fine-tuning the quantizied model.
* The experiment setup (Section 3: Assessing safety risks of quantization) is confusing in some places. It considers the cases where using harmful datasets for quantization, which does not sound realistic. The used datasets for quantization are small in size.
* The experiment of the safety patching method on quantizied models does not include baselines or ablation studies.
* It is unclear about techical contributions of Q-safe as its methodology looks similar to general safety alignment methods, instead of focusing on the quantization models.
We conduct the first systematic safety evaluation for quantization on LLMs and propose a novel safety-patching algorithm for quantized LLMs.
@inproceedings{
anonymous2024qresafe,
title={Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=VarjSNbij7},
note={under review}
}
Quantized large language models (LLMs) have garnered surging demand for broadening the deployment scenarios of LLMs, particularly on resource-constrained applications, which would otherwise be infeasible due to the substantial resource overhead incurred by astronomical model sizes. Propelled by this vast application potential, various quantization techniques have been developed to convert high-precision LLMs into low-precision quantized counterparts, aiming to preserve strong capabilities with reduced bit-widths. Despite achieving promising utility preservation after quantization, current efforts have largely neglected the safety aspect of quantized LLMs, leaving them at risk of engaging in harmful behaviors. Unfortunately, safety is fragile to preserve, as evidenced by recent safety studies on high-precision LLMs, further aggravating concerns about the safety of quantized LLMs. Assessing and restoring the safety capabilities of quantized LLMs has thus become a pressing need.

In this paper, we conduct the first systematic assessment of safety risks in quantized LLMs, scrutinizing four mainstream categories of quantization techniques across diverse settings, including varying quantization bit-widths and different quantization-assisting datasets, through well-established safety measurements. Our empirical evaluation reveals concerning safety degradation across all quantization methods and settings. We therefore propose the first quantization-aware safety patching framework,  Q-resafe, to efficiently restore the safety capabilities of quantized LLMs while avoiding any adverse impact on the utility. Extensive experiments demonstrate that  Q-resafe effectively restores the safety of quantized LLMs obtained from diverse quantization processes, matching the pre-quantization LLMs, even with harmful datasets. We will make our implementation publicly available https://anonymous.4open.science/r/Qresafe-D085/.
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Large Language Models Quantization Safety Alignment
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Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models
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