
## Exploiting LLM Quantization
Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin Vechev
Keywords: 
NeurIPS/2024/Proceedings/95767 - Exploiting LLM Quantization.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[2]

The authors state the implementation link in footnote 1 (https://github.com/eth-sri/llm-quantization-attack). In the readme the outhers state the method, installation instructions, examples on how to use the code with two file links for more, and acknowledgements for other repositories used for their implementations. Two directories are other implementations being used (stated in acknowledgements), so we only evaluated the q_attack folder. Here the code can use some more clear comments. Structure is small.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[5]

(2/2)

The authors use the Code-Alpaca dataset and a citation for another data set. Very little details are given regarding the datasets otherwise. It is stated in appendix A.4. they are public.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[4]

Hyperparameters are discussed in appendix A.2. where they are informally summarised per experiment. a structured summary is missing and no acquisition is specified.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[4]

The authors use code security as a metric (explained and cited). All other metrics are named and cited in sec 4, 4.2. and 4.3. but not all are fully explained. Results are single model/run. Test sets are not completely clear.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[4]

Requries expertise on LLM, quantization and harmful attacks.
