Pruning General Large Language Models into Customized Expert Models
Authors: Yiran Zhao, Guizhen Chen, Kenji Kawaguchi, Lidong Bing, Wenxuan Zhang
Accepted at: acl25

Pruning General Large Language Models into Customized Expert Models

Yiran Zhao1  Guizhen Chen2,3  Kenji Kawaguchi1  Lidong Bing4  Wenxuan Zhang5†
1 National University of Singapore   2 Nanyang Technological University, Singapore
3 DAMO Academy, Alibaba Group, Singapore   4 MiroMind
5 Singapore University of Technology and Design

Abstract
Large language models (LLMs) have revolutionized natural language processing, yet their substantial model sizes often require substantial computational resources. To preserve computing resources and accelerate inference speed, it is crucial to prune redundant parameters, especially for experienced users who often need compact expert models tailored to specific downstream scenarios. However, most existing pruning methods focus on preserving the model’s general capabilities, often requiring extensive post-training or suffering from degraded performance due to coarse-grained pruning. In this work, we design a Custom Pruning method (Cus-Prun) to prune a large general model into a smaller lightweight expert model, which is positioned along the “language”, “domain” and “task” dimensions. By identifying and pruning irrelevant neurons of each dimension, Cus-Prun creates expert models without any post-training. Our experiments demonstrate that Cus-Prun consistently outperforms other methods, achieving minimal loss in both expert and general capabilities across various models from different model families and sizes.1

1 Introduction
Large language models (LLMs) (Achiam et al., 2023; Reid et al., 2024; Dubey et al., 2024; Team et al., 2024) have revolutionized the field of natural language processing (NLP), emerging as powerful tools with widespread applications across various languages (Cui et al., 2023; Yang et al., 2024a; Zhang et al., 2024a), domains (Li et al., 2023a; Roziere et al., 2023; Li et al., 2023b), and tasks (Azerbayev et al., 2024; Alves et al., 2024; Zhang et al., 2024b). However, the impressive performance of LLMs often comes at the cost of immense model sizes, mostly containing billions of parameters and thus demand significant computing resources (Goldstein et al., 2023; Musser, 2023). To address this issue, researchers have recently proposed various model pruning methods for LLMs. These methods aim to reduce model parameters while maintaining the overall performance through techniques such as removal of unimportant structures (Ma et al., 2023; Men et al., 2024; Song et al., 2024), matrix approximation (Sharma et al., 2024; Ashkboos et al., 2024), and extensive post-training after pruning (Wang et al., 2024; Xia et al., 2024).

Most existing pruning methods focus on preserving the general capabilities of the model, often evaluated by broad-spectrum benchmarks like MMLU (Hendrycks et al., 2021). While aiming for overall versatility, they may not align well with real-world user needs for a pruned small model, which are usually more specific and targeted. For instance, a user might require a question-answering model tailored specifically for the education domain in German. Such specialized request in fact aligns well with the fundamental motivation behind pruning: to create a smaller model by eliminating unnecessary parameters. In this context, the notion of “unnecessary” parameters becomes more precise—referring to those parameters that are irrelevant to the particular use case. By selectively pruning these redundant parameters, one can construct a smaller, expert model that is better aligned with specific requirements.

However, current pruning techniques focusing on preserving the general capabilities of LLMs often employ coarse-grained approaches such as removing entire layers or modules (Li et al., 2022; Kurz et al., 2024; Huang et al., 2024). Therefore, it may remove parameters that are critical for specialized downstream scenarios, and thus sometimes require extensive post-training to recover the pruned capabilities (Xia et al., 2024; Muralidharan et al., 2024). On the other hand, the preserved certain
Figure 1: Given a requirement for an expert model across three dimensions (language, domain, and task), Cus–Prun (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes overlapping irrelevant neurons across dimensions to obtain the expert model (right).

capabilities may not be useful or relevant to our desired application scenario. This misalignment with practical requirements, where users need compact ready-to-deploy expert models without retraining, motivates us to design a more fine-grained and expert model targeting approach.

In this work, we introduce a novel Custom Pruning (Cus–Prun) method, designed to prune a large general-purpose model into a small specialized expert model tailored for specific scenarios. To achieve broad adaptability, we define the expert model by positioning the target user’s needs along three key dimensions: language (e.g., English, Chinese, German), domain (e.g., E-commerce, education), and task (e.g., QA, summarization). Inspired by existing studies that certain neurons are responsible for certain functions (Zhao et al., 2024b; Tang et al., 2024; Liang et al., 2024), Cus–Prun identifies and preserves critical neurons that are more relevant to particular languages, domains, or tasks, while pruning less relevant ones, ultimately leading to a smaller expert models. Specifically, as illustrated in Figure 1, Cus–Prun first identifies irrelevant neurons for each dimension by assessing the impact of their removal on the generated output when processing corresponding corpus, which could be easily constructed from the relevant plain text documents. Next, the expert model is constructed by pruning irrelevant neurons across all dimensions. Furthermore, Cus–Prun’s flexibility allows it to focus on one, two, or all three dimensions (language, domain, task) as needed, making it adaptable to a wide range of real-world applications where specialized LLMs are required. Importantly, by performing fine-grained pruning at the neuron level, the method could also retain most of the essential neurons within the model backbone, thereby preserving most general capabilities.

We conduct comprehensive experiments to evaluate the performance of Cus–Prun across various scenarios. Experimental results demonstrate that it consistently outperforms other pruning methods in all settings. For three-dimensional specific expert models, Cus–Prun demonstrates remarkable pruning effectiveness across different model families and sizes, such as Mistral-Nemo-12B, Llama3-8B, Llama2-13B, and Llama3-70B, while maintaining strong expert and general capabilities. The method is evaluated across multilingual, multidomain, and multitask datasets, as well as representative compound NLP benchmarks, and consistently outperforms state-of-the-art pruning methods. Moreover, Cus–Prun is highly adaptable, working effectively across a wide range of pruning ratios, even up to nearly half the parameters, without compromising its superior performance. For more focused applications, such as two- or one-dimensional specific expert models (e.g., language-domain specific or language-specific models), Cus–Prun continues to significantly outperform other pruning methods, showcasing its versatility and effectiveness in diverse specialized settings.

2 Custom Pruning (Cus–Prun)

An expert model could be generally positioned from three dimensions: “language” (\( L \in \mathbb{L} \)), “domain” (\( D \in \mathbb{D} \)), and “task” (\( T \in \mathbb{T} \)), which can be represented as \( LLM_{\text{Exp}} := (L, D, T) \in \mathbb{L} \times \mathbb{D} \times \mathbb{T} \). Specifically, the language dimension encompasses various languages such as English, Spanish, and Thai. The domain dimension covers different fields like finance, legal, and medical. The task dimension includes various applications such as question-answering, data-to-text, and summarization. In this section, we propose a custom pruning method named Cus–Prun to derive smaller expert models with flexible customization granularity.

2.1 Foundational Custom Pruning

Drawing inspiration from recent LLM interpretation studies (Tang et al., 2024; Liang et al., 2024; Zhao et al., 2024b) that many parameters in the
model are redundant to processing a specific “language”, we hypothesize that this phenomenon can be extended to other dimensions such as “domain” and “task”, meaning that certain parameters remain unused when handling a specific dimension. Rather than eliminating entire layers or modules (Song et al., 2024; Men et al., 2024; Zhang et al., 2024c), Cus-Prun performs a fine-grained investigation by identifying and removing redundant neurons (i.e., individual rows or columns in parameter matrices) across all components (e.g., attention and feed-forward layers).

Concretely, when handling each dimension, we identify a specific set of irrelevant neurons in the original LLM, denoted as \( \tilde{\mathcal{N}}_L, \tilde{\mathcal{N}}_D, \) and \( \tilde{\mathcal{N}}_T \) for \( L, D, \) and \( T \), respectively. Specifically, to identify irrelevant neurons corresponding to the selected dimension, we construct a corpus within that dimension while ablating others. For example, to determine irrelevant neurons for a specific language \( L_{\text{Exp}} \), we create a corpus set

\[
C_{L_{\text{Exp}}} = \{(L_{\text{Exp}}, D, T)|D \in \mathbb{D}, T \in \mathbb{T}\},
\]

comprising documents in language \( L_{\text{Exp}} \) across various domains \( D \) and tasks \( T \). We then identify neurons that are consistently irrelevant across all documents in \( C_{L_{\text{Exp}}}\),

\[
\tilde{\mathcal{N}}_{L_{\text{Exp}}} = \{\text{Neuron}| \text{Irrelevant to } c, \forall c \in C_{L_{\text{Exp}}}\},
\]

where a neuron is considered irrelevant if its removal from the parameter matrix affects the generated output below a specified threshold. Formally, for \( i \)-th neuron in layer \( l \), denoted as \( N_i^{(l)} \), its relevance to document \( c \) is measured by \( |h_{N_i^{(l)},i}(c) - h_i(c)|_2 \), where \( h_i(c) \) is the layer output and \( h_{N_i^{(l)},i}(c) \) is the output with the neuron removed. Furthermore, neurons with impact in the lowest \( \sigma \% \) are considered irrelevant, where \( \sigma \) is a pre-defined pruning ratio.

Similarly, we could establish corresponding corpus sets for other dimensions,

\[
C_{D_{\text{Exp}}} = \{(L, D_{\text{Exp}}, T)|L \in \mathbb{L}, T \in \mathbb{T}\},
\]
\[
C_{T_{\text{Exp}}} = \{(L, D, T_{\text{Exp}})|L \in \mathbb{L}, D \in \mathbb{D}\},
\]

to extract irrelevant neurons, \( \tilde{\mathcal{N}}_{D_{\text{Exp}}} \) and \( \tilde{\mathcal{N}}_{T_{\text{Exp}}} \). Finally, the expert model could constructed by

\[
\mathcal{LLM}_{\text{Exp}} = \mathcal{LLM} \ominus \left\{ \tilde{\mathcal{N}}_{L_{\text{Exp}}} \cap \tilde{\mathcal{N}}_{D_{\text{Exp}}} \cap \tilde{\mathcal{N}}_{T_{\text{Exp}}} \right\},
\]

where \( \ominus \) represents removing the corresponding neurons from \( \mathcal{LLM} \). The overall algorithm is further illustrated in Algorithm 1.

Algorithm 1 Adaptive Custom Pruning
Input: Original language model \( \mathcal{LLM} \), request for expert model \( \mathcal{LLM}_{\text{Exp}} \) with selected dimensions: \( L_{\text{Exp}}, D_{\text{Exp}}, T_{\text{Exp}} \) (any subset), request for pruning ratio \( \sigma \).
1: // Construct specific corpora for each selected dimension.
2: \( C = \{ \} \)
3: if \( L_{\text{Exp}} \) is specified then
4: \hspace{1em} \( C = C \cup \{(L_{\text{Exp}}, D, T) \mid D \in \mathbb{D}, T \in \mathbb{T}\} \)
5: end if
6: if \( D_{\text{Exp}} \) is specified then
7: \hspace{1em} \( C = C \cup \{(L, D_{\text{Exp}}, T) \mid L \in \mathbb{L}, T \in \mathbb{T}\} \)
8: end if
9: if \( T_{\text{Exp}} \) is specified then
10: \hspace{1em} \( C = C \cup \{(L, D, T_{\text{Exp}}) \mid L \in \mathbb{L}, D \in \mathbb{D}\} \)
11: end if
12: // Identify irrelevant neurons for each selected dimension.
13: for all neuron \( N_i^{(l)} \) in \( \mathcal{LLM} \) do
14: \hspace{1em} if \( \forall c \in C, N_i^{(l)} \in \tilde{\mathcal{N}}(c) \) then
15: \hspace{2em} \( \tilde{\mathcal{N}} \leftarrow \tilde{\mathcal{N}} \cup N_i^{(l)} \)
16: \hspace{1em} end if
17: end for
18: // Prune irrelevant neurons to obtain expert model.
19: \( \mathcal{LLM}_{\text{Exp}} = \mathcal{LLM} \ominus \tilde{\mathcal{N}} \)
Output: \( \mathcal{LLM}_{\text{Exp}} \)

2.2 Adaptive Custom Pruning

In many applications, the need for expertise might be constrained to one or two dimensions. For example, a language-specific or domain-specific model only requires pruning along a single dimension, while a language-domain-specific model (e.g., a Chinese Medical LLM) constrains two dimensions. In this section, we extend Cus-Prun to support different granularity levels.

Two-Dimensional Specific Expert Model Without losing generality, we use the language-domain expert model as a concrete example, which requires an expert model constrained in two dimensions: language (\( L_{\text{Exp}} \)) and domain (\( D_{\text{Exp}} \)). We similarly derive the sets of irrelevant neurons \( \tilde{\mathcal{N}}_{L_{\text{Exp}}} \) and \( \tilde{\mathcal{N}}_{D_{\text{Exp}}} \), and obtain the expert model by pruning the original dense model as follows:

\[
\mathcal{LLM}_{\text{Exp}} := \mathcal{LLM} \ominus \{ \tilde{\mathcal{N}}_{L_{\text{Exp}}} \cap \tilde{\mathcal{N}}_{D_{\text{Exp}}} \}.
\]

One-Dimensional Specific Expert Model We use the language-specific expert model as an exam-
ple, which focuses on optimizing performance for a certain language (\( L_{\text{Exp}} \)), irrespective of domain or task. Similarly, we obtain the language-specific corpus \( C_{L_{\text{Exp}}} \), then identify irrelevant neurons \( \tilde{\mathcal{N}}_{L_{\text{Exp}}} \) and extract the expert model by
\[
\mathcal{LLM}_{\text{Exp}} := \mathcal{LLM} \ominus \{ \tilde{\mathcal{N}}_{L_{\text{Exp}}} \}.
\]
To enhance efficiency, we implement the parallel neuron-detection method (Zhao et al., 2024b), which accelerates the sequential calculations from line14 to line16 in Algorithm 1.

3 Preliminary Evaluation

In this section, to simulate realistic user-defined requirements where all three dimensions (language, domain, task) are explicitly specified, we conduct preliminary experiments on developing expert models with fine-grained pruning. This setting not only validates our approach under the most demanding conditions but also serves as a prototype for later experiments with one- or two-dimensional customizations.

Experiment Design We consider three scenarios, each named according to the language-domain-task pattern: Korean-Legal-Summarization (Hwang et al., 2022), English-Medical-MCQ (García-Ferrero et al., 2024), and Chinese-E-commerce-Sentiment Analysis (Zhang et al., 2015). These scenarios were selected because each represents a unique combination of language, domain, and task, effectively simulating the diverse needs a user might specify in practice.

For each scenario, we curate the corresponding corpus for each dimension. This curation can be done through manual collection or by automatically retrieving relevant documents online. In this preliminary study, without loss of generality, we employ a strong proprietary model\footnote{https://platform.openai.com/docs/models/gpt-4o} to generate a corpus containing 50 documents for each dimension. Detailed prompts can be found in Appendix A.1. The generated documents could then be used to determine the relevance of neurons for each dimension of each scenario.

Experiment Setup We use Llama3-8B (Dubey et al., 2024) as the original dense model and follow the typical setting to set the pruning ratio as 25%. Performance is evaluated using Rouge-L (Lin, 2004) for Korean-Legal-Summary and accuracy score for another two tasks. For comparison, we adopt SliceGPT (Ashkboos et al., 2024) as a baseline, which replaces each weight matrix with a smaller proxy matrix without considering the specific use-case requirements.

Main Results Figure 2 presents the results and one concrete example for the original dense model, pruned model with SliceGPT, and pruned model with our proposed Cus–Prun method across the three scenarios. We observe that Cus–Prun largely preserves the performance of the dense model, retraining 92%, 83%, and 94% of the original dense model performance on these three cases respectively. In contrast, the baseline method SliceGPT, which does not consider specific use cases, largely underperforms compared to Cus–Prun. Overall, the results demonstrate that our proposed Cus–Prun method could effectively obtain expert models tailored to specific use cases across different languages, domains, and tasks that maintain high performance despite substantial pruning.

Figure 2: Concrete examples of applying Cus–Prun to prune 25% of Llama3-8B-Base’s parameters into three-dimensional expert models. Numbers above each box indicate performance on the whole test set, with the first evaluated by Rouge-L, and the other two by accuracy.
Table 1: Main Results of Cus–Prun on multilingual setting with a pruning ratio of 25%, where “general capability” is tested in English and averaged across several expert models, while “specific capability” is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (%) for other datasets.

<table>
  <tr>
    <th rowspan="2">Method</th>
    <th colspan="2">General Capability</th>
    <th colspan="2">Multilingual Expert</th>
    <th colspan="2">Multidomain Expert</th>
    <th colspan="2">Multitask Expert</th>
  </tr>
  <tr>
    <th>ARC-c GSM8K MMLU Avg.</th>
    <th>GMGSM M3 XQuad Sum Avg.</th>
    <th>MMQC FTQA TSA AMSA Avg.</th>
    <th>MSum ASum AMCF Avg.</th>
    <th>LLMPrun</th>
    <th>SliceGPT</th>
    <th>ShortGPT</th>
    <th>Cus–Prun</th>
  </tr>
  <tr>
    <td>Dense</td>
    <td>70.7</td>
    <td>58.3</td>
    <td>63.1</td>
    <td>64.1</td>
    <td>41.2</td>
    <td>49.1</td>
    <td>63.4</td>
    <td>32.9</td>
    <td>46.7</td>
    <td>51.8</td>
    <td>23.9</td>
    <td>67.1</td>
    <td>95.9</td>
    <td>59.8</td>
    <td>76.6</td>
    <td>16.2</td>
    <td>78.2</td>
    <td>57.0</td>
  </tr>
  <tr>
    <td>LLMPrun</td>
    <td>26.3</td>
    <td>2.5</td>
    <td>24.2</td>
    <td>17.7</td>
    <td>1.1</td>
    <td>24.0</td>
    <td>13.6</td>
    <td>23.2</td>
    <td>15.1</td>
    <td>(0)</td>
    <td>61.8</td>
    <td>76.0</td>
    <td>34.5</td>
    <td>62.2</td>
    <td>21.8</td>
    <td>80.0</td>
    <td>54.7</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>41.5</td>
    <td>0.0</td>
    <td>24.2</td>
    <td>21.7</td>
    <td>0.0</td>
    <td>14.9</td>
    <td>16.6</td>
    <td>8.5</td>
    <td>10.0</td>
    <td>22.6</td>
    <td>0.0</td>
    <td>41.2</td>
    <td>47.7</td>
    <td>29.4</td>
    <td>7.3</td>
    <td>29</td>
    <td>51.3</td>
    <td>20.5</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>38.3</td>
    <td>0.0</td>
    <td>28.6</td>
    <td>22.3</td>
    <td>0.0</td>
    <td>26.9</td>
    <td>0.0</td>
    <td>2.7</td>
    <td>7.4</td>
    <td>3.2</td>
    <td>(0)</td>
    <td>38.6</td>
    <td>35.7</td>
    <td>19.4</td>
    <td>4.1</td>
    <td>4.8</td>
    <td>43.8</td>
    <td>17.6</td>
  </tr>
  <tr>
    <td><b>Cus–Prun</b></td>
    <td><b>62.4</b></td>
    <td><b>37.0</b></td>
    <td><b>54.7</b></td>
    <td><b>51.4</b></td>
    <td><b>30.1</b></td>
    <td><b>41.5</b></td>
    <td><b>52.6</b></td>
    <td><b>31.5</b></td>
    <td><b>38.9</b></td>
    <td><b>42.9</b></td>
    <td><b>20.6</b></td>
    <td><b>61.8</b></td>
    <td><b>87.6</b></td>
    <td><b>55.2</b></td>
    <td><b>68.4</b></td>
    <td><b>12.8</b></td>
    <td><b>75.5</b></td>
    <td><b>52.2</b></td>
  </tr>
  <tr>
    <td>Dense</td>
    <td>82.6</td>
    <td>68.5</td>
    <td>50.4</td>
    <td>67.2</td>
    <td>51.7</td>
    <td>43.8</td>
    <td>49.2</td>
    <td>25.4</td>
    <td>42.5</td>
    <td>54.6</td>
    <td>26.6</td>
    <td>69.4</td>
    <td>92.4</td>
    <td>60.8</td>
    <td>88.7</td>
    <td>3.0</td>
    <td>78.6</td>
    <td>56.4</td>
  </tr>
  <tr>
    <td>LLMPrun</td>
    <td>22.5</td>
    <td>2.7</td>
    <td>30.7</td>
    <td>18.6</td>
    <td>2.1</td>
    <td>27.8</td>
    <td>19.0</td>
    <td>23.2</td>
    <td>18.0</td>
    <td>0.0</td>
    <td>51.0</td>
    <td>20.9</td>
    <td>18.0</td>
    <td>59.3</td>
    <td>0.5</td>
    <td>2.8</td>
    <td>20.9</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>49.4</td>
    <td>1.9</td>
    <td>32.1</td>
    <td>27.8</td>
    <td>0.8</td>
    <td>25.1</td>
    <td>17.4</td>
    <td>7.8</td>
    <td>12.8</td>
    <td>24.9</td>
    <td>9.2</td>
    <td>34.2</td>
    <td>54.3</td>
    <td>30.7</td>
    <td>27.4</td>
    <td>1.3</td>
    <td>36.3</td>
    <td>21.7</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>37.8</td>
    <td>0.0</td>
    <td>33.9</td>
    <td>23.9</td>
    <td>2.9</td>
    <td>27.0</td>
    <td>18.0</td>
    <td>5.0</td>
    <td>13.2</td>
    <td>31.4</td>
    <td>7.2</td>
    <td>39.2</td>
    <td>52.5</td>
    <td>32.6</td>
    <td>26.2</td>
    <td>0.2</td>
    <td>42.7</td>
    <td>23.0</td>
  </tr>
  <tr>
    <td><b>Cus–Prun</b></td>
    <td><b>67.5</b></td>
    <td><b>43.4</b></td>
    <td><b>43.8</b></td>
    <td><b>51.6</b></td>
    <td><b>34.3</b></td>
    <td><b>39.2</b></td>
    <td><b>40.7</b></td>
    <td><b>23.1</b></td>
    <td><b>34.3</b></td>
    <td><b>47.9</b></td>
    <td><b>25.1</b></td>
    <td><b>67.3</b></td>
    <td><b>83.7</b></td>
    <td><b>56.0</b></td>
    <td><b>83.5</b></td>
    <td><b>3.4</b></td>
    <td><b>72.8</b></td>
    <td><b>50.9</b></td>
  </tr>
  <tr>
    <td>Dense</td>
    <td>50.3</td>
    <td>31.4</td>
    <td>53.4</td>
    <td>45.1</td>
    <td>17.5</td>
    <td>30.4</td>
    <td>44.1</td>
    <td>24.9</td>
    <td>29.2</td>
    <td>25.2</td>
    <td>0.0</td>
    <td>42.7</td>
    <td>84.1</td>
    <td>38.0</td>
    <td>70.0</td>
    <td>7.4</td>
    <td>44.3</td>
    <td>40.6</td>
  </tr>
  <tr>
    <td>LLMPrun</td>
    <td>22.4</td>
    <td>2.1</td>
    <td>23.6</td>
    <td>16.0</td>
    <td>1.1</td>
    <td>22.8</td>
    <td>3.8</td>
    <td>17.7</td>
    <td>11.3</td>
    <td>0.0</td>
    <td>9.7</td>
    <td>0.0</td>
    <td>2.4</td>
    <td>21.6</td>
    <td>4.8</td>
    <td>0.0</td>
    <td>8.8</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>45.9</td>
    <td>2.4</td>
    <td>48.7</td>
    <td>32.3</td>
    <td>2.8</td>
    <td>25.3</td>
    <td>23.4</td>
    <td>9.9</td>
    <td>15.5</td>
    <td>18.7</td>
    <td>0.0</td>
    <td>28.4</td>
    <td>67.3</td>
    <td>28.6</td>
    <td>24.5</td>
    <td>4.9</td>
    <td>32.9</td>
    <td>20.8</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>39.5</td>
    <td>3.8</td>
    <td>37.2</td>
    <td>26.8</td>
    <td>2.4</td>
    <td>23.0</td>
    <td>24.7</td>
    <td>11.3</td>
    <td>15.3</td>
    <td>16.9</td>
    <td>0.0</td>
    <td>34.6</td>
    <td>69.8</td>
    <td>30.3</td>
    <td>23.8</td>
    <td>5.2</td>
    <td>39.1</td>
    <td>22.7</td>
  </tr>
  <tr>
    <td><b>Cus–Prun</b></td>
    <td><b>48.3</b></td>
    <td><b>20.8</b></td>
    <td><b>50.0</b></td>
    <td><b>39.7</b></td>
    <td><b>12.7</b></td>
    <td><b>26.2</b></td>
    <td><b>34.2</b></td>
    <td><b>24.1</b></td>
    <td><b>24.3</b></td>
    <td><b>25.6</b></td>
    <td><b>0.0</b></td>
    <td><b>38.5</b></td>
    <td><b>68.3</b></td>
    <td><b>33.1</b></td>
    <td><b>64.5</b></td>
    <td><b>6.7</b></td>
    <td><b>42.9</b></td>
    <td><b>38.0</b></td>
  </tr>
  <tr>
    <td>Dense</td>
    <td>84.1</td>
    <td>82.7</td>
    <td>78.8</td>
    <td>81.9</td>
    <td>69.5</td>
    <td>71.1</td>
    <td>69.1</td>
    <td>36.6</td>
    <td>61.6</td>
    <td>72.1</td>
    <td>55.3</td>
    <td>83.6</td>
    <td>96.2</td>
    <td>76.8</td>
    <td>84.2</td>
    <td>17.3</td>
    <td>81.8</td>
    <td>61.1</td>
  </tr>
  <tr>
    <td>LLMPrun</td>
    <td>69.1</td>
    <td>26.0</td>
    <td>53.2</td>
    <td>49.4</td>
    <td>16.8</td>
    <td>43.7</td>
    <td>43.0</td>
    <td>29.0</td>
    <td>33.1</td>
    <td>27.3</td>
    <td>1.0</td>
    <td>51.0</td>
    <td>50.3</td>
    <td>32.4</td>
    <td>10.2</td>
    <td>13.7</td>
    <td>20.6</td>
    <td>14.8</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>65.7</td>
    <td>0.0</td>
    <td>54.2</td>
    <td>40.0</td>
    <td>3.7</td>
    <td>44.8</td>
    <td>33.0</td>
    <td>21.2</td>
    <td>25.7</td>
    <td>57.6</td>
    <td>27.6</td>
    <td>68.1</td>
    <td>59.4</td>
    <td>53.2</td>
    <td>58.0</td>
    <td>14.2</td>
    <td>68.3</td>
    <td>46.8</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>59.4</td>
    <td>5.6</td>
    <td>75.5</td>
    <td>46.8</td>
    <td>11.9</td>
    <td>43.1</td>
    <td>38.8</td>
    <td>24.0</td>
    <td>29.5</td>
    <td>58.4</td>
    <td>32.2</td>
    <td>67.5</td>
    <td>64.9</td>
    <td>55.8</td>
    <td>59.6</td>
    <td>13.9</td>
    <td>65.8</td>
    <td>46.4</td>
  </tr>
  <tr>
    <td><b>Cus–Prun</b></td>
    <td><b>68.4</b></td>
    <td><b>53.2</b></td>
    <td><b>66.6</b></td>
    <td><b>62.7</b></td>
    <td><b>43.1</b></td>
    <td><b>57.7</b></td>
    <td><b>59.8</b></td>
    <td><b>34.3</b></td>
    <td><b>48.7</b></td>
    <td><b>68.2</b></td>
    <td><b>43.9</b></td>
    <td><b>81.4</b></td>
    <td><b>87.8</b></td>
    <td><b>70.3</b></td>
    <td><b>80.4</b></td>
    <td><b>15.7</b></td>
    <td><b>77.5</b></td>
    <td><b>57.9</b></td>
  </tr>
</table>

4 Foundational Custom Pruning Assessment

As demonstrated by preliminary evaluation in Section 3, Cus–Prun enables the creation of expert models tailored to specific languages, domains, and tasks. However, existing benchmark datasets may not always align with such specialized requirements for a systematic evaluation. To simplify our evaluation without losing generality, we use two distinct corpora: one focusing independently on a single dimension and another encompassing the remaining two dimensions. This approach allows us to evaluate Cus–Prun’s performance in multilingual, multidomain, and multitask settings.

Formally, in the multilingual setting, instead of constructing \( C_{L_{\text{Exp}}} \), \( C_{D_{\text{Exp}}} \) and \( C_{T_{\text{Exp}}} \) independently, we can construct two corpora, \( C_{L_{\text{Exp}}} \) and \( C_{(D,T)_{\text{Exp}}} \), where \( C_{L_{\text{Exp}}} \) helps to identify irrelevant neurons in a specific language (\( \tilde{\mathcal{N}}_{L_{\text{Exp}}} \)) and \( C_{(D,T)_{\text{Exp}}} \) helps to identify irrelevant neurons in a specific domain-task combination (\( \tilde{\mathcal{N}}_{D_{\text{Exp}} \cap T_{\text{Exp}}} \)). Formally speaking, Cus–Prun in Equation 5 is transferred to

\[
\mathcal{L}_{\text{LMExp}} = \mathcal{L}_{\text{LM}} \ominus \left( \tilde{\mathcal{N}}_{L_{\text{Exp}}} \cap ( \tilde{\mathcal{N}}_{D_{\text{Exp}}} \cap \tilde{\mathcal{N}}_{T_{\text{Exp}}} ) \right)
\]
\[
\equiv \mathcal{L}_{\text{LM}} \ominus \left( \tilde{\mathcal{N}}_{L_{\text{Exp}}} \cap \tilde{\mathcal{N}}_{D_{\text{Exp}} \cap T_{\text{Exp}}} \right).
\]

Note that this simplification is also applicable to \( C_{D_{\text{Exp}}}, C_{(L,T)_{\text{Exp}}} \) and \( C_{T_{\text{Exp}}}, C_{(L,D)_{\text{Exp}}} \).

4.1 Experiment Setup

Benchmarks Although the primary goal of Cus–Prun is to create expert LLMs tuned to specific use cases, we also evaluate the pruned models on standard general capabilities to ensure minimal performance loss. Specifically, we employ ARC-Challenge (Clark et al., 2018) (5-shots), GSM8K (Cobbe et al., 2021) (5-shots with CoT prompting (Wei et al., 2022)), and MMLU (Hendrycks et al., 2021) (5-shots) to represent models general capability. Note that we utilize a generation task and implement CoT prompting method, a more challenging setting that has not been previously evaluated by existing pruning techniques (Song et al., 2024; Sharma et al., 2024; Yang et al., 2024b; Zhang et al., 2024c).

Baselines We employ several state-of-the-art pruning methods as the baseline that do not require post-training after pruning the model. (i) Dense represents the original model without pruning; (ii) LLM-Pruner (Ma et al., 2023) adopts structural pruning that selectively removes non-critical coupled structures based on gradient information;3 (iii) SliceGPT (Ashkboos et al., 2024) replaces each weight matrix with a smaller dense matrix, reducing the embedding dimension of the network; (iv) ShortGPT (Men et al., 2024) directly deletes the redundant layers in LLMs based on an importance

3To ensure a fair comparison, we evaluate its performance before post-training, following Men et al. (2024).
score. We follow the typical pruning setting from these previous studies to set the pruning ratio to 25% for all methods and all models.

Backbone Models We choose 4 models from different model series and different sizes, including Llama3-8B-Base (Dubey et al., 2024), Mistral-Nemo-Base-2407\(^4\) (short as Mistral-12B), Llama2-13B-Base (Touvron et al., 2023), Llama3-70B-Base (Dubey et al., 2024).

4.2 Multilingual Setting

Dataset We employ multiple representative multilingual datasets for multilingual setting, which covers reasoning (MGSM (Shi et al., 2023), 5-shots), multilingual knowledge (M3Exam (Zhang et al., 2023), 3-shots, abbreviated as M3), understanding (XQuAD (Artetxe et al., 2020), 5-shots), and generation (XLSum (Hasan et al., 2021), zero-shots, abbreviated as Sum). Furthermore, we consider three languages spanning a range from high-resource to low-resource including German (De), Chinese (Zh) and Thai (Th). More detailed experiment settings are explained in Appendix A.3.1.

Main Results Table 1 summarizes the performance of Cus-Prun on multilingual datasets, with average scores across languages. Detailed breakdown results for each language are shown in Table 5, Table 6 and Table 7 in Appendix A.2. We can observe that Cus-Prun consistently outperforms baseline pruning methods in achieving expert capabilities while preserving general performance. For example, on Llama3-8B, Cus-Prun achieves a score of 38.9 compared to at most 15.5 for other methods. Similar improvements are observed for Mistral-12B (34.3 vs. 18.0), Llama2-13B (24.3 vs. 15.5), and Llama3-70B (48.7 vs. 33.1). We can also see that the generation tasks are especially challenging; for instance, Cus-Prun achieves a score of 30.1 on MGSM for Llama3-8B, while other methods nearly lose the ability to generate coherent reasoning outputs (often approaching 0 accuracy for all but the largest model). Additionally, Cus-Prun performs robustly across both high-resource and low-resource languages. Overall, Cus-Prun excels in both performance and robustness across diverse tasks and languages.

4.3 MultiDomain Setting

Dataset For the multidomain setting, we employ several domain-specific datasets, including medical domain multiply choices questions (MedMCQ (Pal et al., 2022), 3-shots, abbreviated as MMCQ), finance domain table question-answering (FinTQA (Chen et al., 2021), 8-shots, abbreviated as FTQA), social media domain sentiment analysis (TSA (Kharde and Sonawane, 2016), 3-shots), and e-commerce domain sentiment analysis (AMSA (Zhang et al., 2015), 3-shots). Moreover, in multidomain setting, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix A.3.2.

Main Results Table 1 shows the performance of Cus-Prun on multidomain setting. We find that Cus-Prun consistently outperforms other pruning methods in both expert and general capabilities. For expert capabilities, Cus-Prun achieves a score of 53.2 on Llama3-8B, while other pruning methods achieve at most 34.5. Similar improvements are observed for Mistral-12B (56.0 vs. 32.6), Llama2-13B (33.1 vs. 30.3), and Llama3-70B (70.3 vs. 55.8).

4.4 MultiTask Setting

Dataset For the multitask setting, we employ several task-specific datasets, including the medical summarization task (MedSum (Abacha and Demner-Fushman, 2019), 3-shots, abbreviated as MSum), summarization task in e-commerce (Amazon Summary (Wang et al., 2022; Brüel-Gabrielsson et al., 2024), 3-shots, abbreviated as ASum), counterfactual task in e-commerce (Amazon Counterfactual (O’Neill et al., 2021), 3-shots, abbreviated as AMCF). Similarly, in multitask setting scenarios, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix A.3.3.

Main Results Table 1 shows the performance of Cus-Prun on multitask setting. We find that except for LLM-Pruner under Llama3-8B, Cus-Prun outperforms other pruning methods in both expert and general capabilities. For expert tasks, Cus-Prun achieves a score of 50.9 on Mistral-12B, significantly outperforming the highest score of 23.0 from other methods. Likewise, it attains 38.0 on Llama2-13B and 57.9 on Llama3-70B, both markedly higher than the corresponding maximum scores of 22.7 and 46.8
Table 2: Performance of Chinese-Medical expert model on MCQ task

<table>
  <tr>
    <th>Method</th>
    <th>General</th>
    <th>CMExam</th>
  </tr>
  <tr>
    <td>Dense</td>
    <td>59.3</td>
    <td>50.6</td>
  </tr>
  <tr>
    <td>LLM-Pruner</td>
    <td>18.6</td>
    <td>25.0</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>27.8</td>
    <td>26.9</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>23.9</td>
    <td>23.7</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td><b>52.4</b></td>
    <td><b>48.7</b></td>
  </tr>
</table>

Figure 3: Chinese Medical LLM performance. Numbers are quality on the whole test set evaluated by GPT4.

achieved by competing methods.

4.5 Analysis on Aggressive Pruning Ratio
To optimize for specialized tasks rather than maintaining general capabilities, we employ more aggressive pruning ratios. We combine layer pruning with our custom neuron pruning method in Algorithm 1 and evaluate the approach on M3Exam, MedMCQ, and Amazon Counterfactual (AMCon-tFact) datasets using Llama3-8B. Detailed results are shown in Table 3. We find that Cus-Prun consistently maintains the model’s capabilities even at higher pruning ratios. Specifically, when the pruning ratio is increased to 45%, ShortGPT nearly loses the capability of generating meaningful answers, while Cus-Prun still achieves scores of 48.4 on MMLU and 50.6 on expert capabilities.

Table 3: Aggressive pruning ratio on Llama3-8B.

<table>
  <tr>
    <th>Method</th>
    <th>Ratio</th>
    <th>Speedup</th>
    <th>MMLU</th>
    <th>Expert</th>
  </tr>
  <tr>
    <td>Dense</td>
    <td>0.0</td>
    <td>1×</td>
    <td>63.1</td>
    <td>59.7</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>25.0</td>
    <td>1.3×</td>
    <td>28.6</td>
    <td>24.6</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>25.0</td>
    <td>1.3×</td>
    <td><b>51.9</b></td>
    <td><b>53.3</b></td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>34.2</td>
    <td>1.5×</td>
    <td>20.8</td>
    <td>18.5</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>35.0</td>
    <td>1.5×</td>
    <td><b>50.2</b></td>
    <td><b>51.4</b></td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>43.8</td>
    <td>1.8×</td>
    <td>7.9</td>
    <td>10.2</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>45.0</td>
    <td>1.8×</td>
    <td><b>48.4</b></td>
    <td><b>50.6</b></td>
  </tr>
</table>

5 Adaptive Custom Pruning Assessment
In this section, we evaluate the generality of Cus-Prun in dynamic scenarios, including specific expert models in two and one dimensions, as described in Section 2.2.

5.1 Two Dimensions Specific Expert Model
Experiment Settings We use the Chinese-Medical setting as a concrete example of a two-dimensional expert model designed to perform a wide range of medical tasks in Chinese. We adopt Mistral-12b as the backbone model and utilize corpus from Wikipedia for Chinese content and general medical corpus for medical knowledge. The performance of the target Chinese-Medical expert model is evaluated on two datasets: CMExam (Liu et al., 2023) (5-shots), a Chinese medical multiple-choice question dataset, and HuatuoQA (Li et al., 2023a), a Chinese medical question-answering dataset. We assess the performance on CMExam using accuracy metrics. For the latter, we sample a sub-testset of size 100 and use GPT-4 as the evaluator, which assigns a score from 0 to 5, representing its quality from low to high. Detailed prompts are listed in Appendix A.1.

Main Results Table 2 presents the performance of the Chinese-Medical LLM on CMExam and its general capabilities. Our results indicate that the expert model pruned using Cus-Prun outperforms models obtained through other pruning methods. Specifically, Cus-Prun achieves a score of 48.7 on CMExam, while its general capability score is 52.4. These results compare favorably to the dense model, which scores 50.6 on CMExam and 59.3 on general capabilities. On the contrary, other pruning methods nearly lose the general and specific capabilities. Furthermore, Figure 3 shows a concrete example of Chinese-Medical LLM performance on medical question-answering. We find that Cus-Prun can produce smaller expert models that maintain their expert capabilities, as demonstrated by its performance score of 2.9/5.0 compared to 3.2/5.0 for the dense model.

5.2 One Dimension Specific Expert Model
Experiment Settings For evaluating the pruning method under a one-dimensional expert model setting, we focus on language-specific pruning, showing how to transform a dense model into
language-specific variants. We consider three linguistically diverse languages: German, Chinese, and Thai. We conduct experiments based on the Llama3-8b model. To identify language-specific (while domain- and task-agnostic) neurons, we employ a diverse range of corpora, including Wikipedia, MGSM, and M3Exam, ensuring coverage of various domains and tasks. The effectiveness of our pruning technique is then evaluated using three held-out multilingual datasets including XQuAD (Artetxe et al., 2020), XNLI (Conneau et al., 2018), and XSum (Narayan et al., 2018).

Main Results Figure 4 illustrates the performance of language-specific models using Cus–Prun. By pruning 25% of the neurons from the original model, Cus–Prun not only retains general performance but also preserves language-specific capabilities. For instance, the German-specific model scores 54.7 in general capabilities, 48.3 on XQuAD, and 56.8 on XNLI, compared to the dense model’s scores of 64.1, 52.9, and 62.0, respectively. This trend is consistent for Chinese and Thai models as well. In contrast, ShortGPT struggles to maintain the model’s capabilities, particularly in XQuAD and XLSum, which require generative abilities.

6 Related Work

LLM Compression Given the high costs of training, inferencing, and tuning LLMs, many studies focus on model compression methods, including compression (Zhu et al., 2023), quantization (Xu et al., 2023; Dettmers et al., 2024; Lin et al., 2024; Li et al., 2024), and pruning (Wang et al., 2019). Sparsity-based structural pruning enhances GPU efficiency with sparse structures but doesn’t always reduce parameter counts (Li et al., 2022, 2023c; Kurz et al., 2024; Zhao et al., 2024a; Huang et al., 2024). Unstructured pruning reduces parameters while maintaining performance, using post-training techniques (Ma et al., 2023; Xia et al., 2024; Muralidharan et al., 2024) or coarse methods like parameter approximation (Zhao et al., 2024a), layer removal (Men et al., 2024), or structural elimination (Zhang et al., 2024c). However, these often fail to preserve domain- or task-specific capabilities, limiting their utility for specialized scenarios.

Customizing Model Customizing LLMs is essential for addressing language-specific challenges, domain-specific needs, and task-specific applications (Cui et al., 2023; Yang et al., 2024b; Li et al., 2023a; Roziere et al., 2023; Li et al., 2023b; Azerbayev et al., 2024; Alves et al., 2024; Zhang et al., 2024b). However, customization often demands extensive fine-tuning with curated data, making it resource-intensive. This highlights the need for efficient methods to quickly create robust, expert models tailored to diverse industries without compromising quality.

7 Conclusion

LLMs deliver impressive capabilities but incur high computational costs. Efficient pruning of redundant parameters is vital for conserving resources and improving inference speed, especially for specialized models. Our method, Cus–Prun, generates smaller expert models without post-training by pruning irrelevant neurons across “language”, “domain”, and “task” dimensions. This finer-grained approach outperforms existing techniques on three-dimensional models and can adapt to realistic scenarios, such as language-domain or language-specific models.

![Bar charts comparing performance of Dense, Custom-Pruning, and ShortGPT for German, Chinese, and Thai datasets](page_120_120_1208_312.png)

Figure 4: Performance of Cus–Prun in obtaining language-specific models.
Limitation

Despite the promising results of Cus-Prun, several limitations should be noted. First, while our method leverages three dimensions (language, domain, and task) for pruning, certain crucial restrictions cannot be fully captured within this framework, such as variations in query format or input structure. Second, whether pruned base models can effectively undergo post-training remains an open question that requires further investigation. This uncertainty about post-training capabilities could limit the model’s adaptability to new scenarios or requirements after pruning. These limitations suggest important directions for future research, including exploring additional dimensions for more comprehensive pruning strategies and investigating the relationship between pruning and post-training effectiveness.

Acknowledgement

This research is supported by the Ministry of Education, Singapore, under its Academic Research Fund (AcRF) Tier 1 grant, and funded through the SUTD Assistant Professorship Scheme (SAP 2025_001). This material is based upon work partially supported by the Air Force Office of Scientific Research under award number FA2386-24-1-4011, and this research is partially supported by the Singapore Ministry of Education Academic Research Fund Tier 1 (Award No: T1 251RES2207).

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A Appendix

A.1 GPT-4o Prompts

The prompts used for generating documents and evaluating answer quality are presented in Table 4.

<table>
  <tr>
    <th>Task</th>
    <th>Prompt</th>
  </tr>
  <tr>
    <td>Generation</td>
    <td>Generate a text document in {language}/{domain}/{task}. Make sure the documents is not fixed to one {language}/{domain}/{task} or {language}/{domain}/{task}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary.</td>
  </tr>
  <tr>
    <td>Evaluation</td>
    <td>Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: {question} Answer: {answer}.</td>
  </tr>
</table>

Table 4: GPT-4o prompts for generating documents and evaluating answer quality.

A.2 Detailed Results for Multilingual

Detailed results for multilingual settings can be found in Table 5, Table 6 and Table 7 for German, Chinese and Thai, respectively.

A.3 Experiments Detailed Settings

A.3.1 Multilingual Settings

Experiment Details For multilingual setting, we can obtain two corpora: \( C_{L_{\text{Exp}}} \) and \( C_{(D,T)_{\text{Exp}}} \). The first corpus contains samples in a specific language across various domains and tasks, while the second corpus contains samples from a specific domain-task combination in other languages, i.e., the target dataset in other languages. Specifically, for \( C_{L_{\text{Exp}}} \) we employ Wikipedia\(^5\) to construct language-specific corpus covering various domains and tasks. For \( C_{(D,T)_{\text{Exp}}} \), we employ the corresponding datasets in English, including GSM8K (Cobbe et al., 2021) for MGSM, the English split of M3Exam\(^6\) for M3Exam, SQuAD (Rajpurkar, 2016) for XQuAD, and XSum (Narayan et al., 2018) for XLSum.

Hyperparameters, including the sizes of \( C_{L_{\text{Exp}}} \) and \( C_{(D,T)_{\text{Exp}}} \), are determined using the validation set of the XLSum dataset and then applied to test-sets in other multilingual datasets. Furthermore,

5 https://huggingface.co/datasets/wikipedia/wikipedia
6 M3Exam is language-specific and does not utilize a translated parallel corpus.
Table 5: Main Results of Cus-Prun on Germany with a pruning ratio of 25%, where “general capability” is tested in English and averaged across several expert models, while “specific capability” is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (%) for other datasets.

<table>
  <tr>
    <th rowspan="2">Model</th>
    <th rowspan="2">Method</th>
    <th colspan="4">General Capability</th>
    <th colspan="5">Expert Capability</th>
  </tr>
  <tr>
    <th>ARC-c</th>
    <th>GSM8K</th>
    <th>MMLU</th>
    <th>Avg.</th>
    <th>MGSM</th>
    <th>M3Exam</th>
    <th>XQuAD</th>
    <th>XLSum</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td rowspan="5">Llama3-8B</td>
    <td>Dense</td>
    <td>70.7</td>
    <td>58.3</td>
    <td>63.1</td>
    <td>64.1</td>
    <td>44.8</td>
    <td>-</td>
    <td>52.9</td>
    <td>-</td>
    <td>48.8</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>26.3</td>
    <td>2.5</td>
    <td>24.2</td>
    <td>17.7</td>
    <td>0.0</td>
    <td>-</td>
    <td>11.0</td>
    <td>-</td>
    <td>5.5</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>41.5</td>
    <td>0.0</td>
    <td>24.2</td>
    <td>21.9</td>
    <td>0.0</td>
    <td>-</td>
    <td>9.8</td>
    <td>-</td>
    <td>4.9</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>38.3</td>
    <td>0.0</td>
    <td>28.6</td>
    <td>22.3</td>
    <td>0.0</td>
    <td>-</td>
    <td>0.0</td>
    <td>-</td>
    <td>0.0</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>61.4</td>
    <td>38.9</td>
    <td>54.5</td>
    <td><b>51.6</b></td>
    <td>32.8</td>
    <td>-</td>
    <td>49.6</td>
    <td>-</td>
    <td><b>41.2</b></td>
  </tr>
  <tr>
    <td rowspan="5">Mistral-12B</td>
    <td>Dense</td>
    <td>82.6</td>
    <td>68.5</td>
    <td>50.4</td>
    <td>59.3</td>
    <td>56.8</td>
    <td>-</td>
    <td>41.2</td>
    <td>-</td>
    <td>49.0</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>22.5</td>
    <td>2.7</td>
    <td>30.7</td>
    <td>18.6</td>
    <td>2.4</td>
    <td>-</td>
    <td>13.4</td>
    <td>-</td>
    <td>7.9</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>49.4</td>
    <td>1.9</td>
    <td>32.1</td>
    <td>27.8</td>
    <td>0.8</td>
    <td>-</td>
    <td>15.5</td>
    <td>-</td>
    <td>8.2</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>37.8</td>
    <td>0.0</td>
    <td>33.9</td>
    <td>23.9</td>
    <td>3.6</td>
    <td>-</td>
    <td>20.3</td>
    <td>-</td>
    <td>12.0</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>64.6</td>
    <td>39.7</td>
    <td>43.2</td>
    <td><b>49.2</b></td>
    <td>31.6</td>
    <td>-</td>
    <td>35.9</td>
    <td>-</td>
    <td><b>33.8</b></td>
  </tr>
  <tr>
    <td rowspan="5">Llama2-13B</td>
    <td>Dense</td>
    <td>50.3</td>
    <td>31.4</td>
    <td>53.4</td>
    <td>45.1</td>
    <td>24.4</td>
    <td>-</td>
    <td>40.3</td>
    <td>-</td>
    <td>32.3</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>22.4</td>
    <td>2.1</td>
    <td>23.6</td>
    <td>16.0</td>
    <td>2.0</td>
    <td>-</td>
    <td>5.7</td>
    <td>-</td>
    <td>3.9</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>45.9</td>
    <td>2.4</td>
    <td>48.7</td>
    <td>32.3</td>
    <td>3.6</td>
    <td>-</td>
    <td>18.1</td>
    <td>-</td>
    <td>10.9</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>39.5</td>
    <td>3.8</td>
    <td>37.2</td>
    <td>26.8</td>
    <td>2.8</td>
    <td>-</td>
    <td>27.2</td>
    <td>-</td>
    <td>15.0</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>47.6</td>
    <td>19.8</td>
    <td>49.9</td>
    <td><b>39.1</b></td>
    <td>18.4</td>
    <td>-</td>
    <td>31.7</td>
    <td>-</td>
    <td><b>25.0</b></td>
  </tr>
  <tr>
    <td rowspan="5">Llama3-70B</td>
    <td>Dense</td>
    <td>84.1</td>
    <td>82.7</td>
    <td>78.8</td>
    <td>81.9</td>
    <td>74.8</td>
    <td>-</td>
    <td>58.2</td>
    <td>-</td>
    <td>66.5</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>69.1</td>
    <td>26.0</td>
    <td>53.2</td>
    <td>49.4</td>
    <td>18.0</td>
    <td>-</td>
    <td>27.3</td>
    <td>-</td>
    <td>22.7</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>65.7</td>
    <td>0.0</td>
    <td>54.2</td>
    <td>40.0</td>
    <td>0.0</td>
    <td>-</td>
    <td>17.3</td>
    <td>-</td>
    <td>8.7</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>59.4</td>
    <td>5.6</td>
    <td>75.5</td>
    <td>46.8</td>
    <td>9.6</td>
    <td>-</td>
    <td>31.5</td>
    <td>-</td>
    <td>20.6</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>66.8</td>
    <td>59.3</td>
    <td>69.1</td>
    <td><b>65.1</b></td>
    <td>48.2</td>
    <td>-</td>
    <td>53.9</td>
    <td>-</td>
    <td><b>51.1</b></td>
  </tr>
</table>

Table 6: Main Results of Cus-Prun on Chinese with a pruning ratio of 25%, where “general capability” is tested in English and averaged across several expert models, while “specific capability” is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (%) for other datasets.

<table>
  <tr>
    <th rowspan="2">Model</th>
    <th rowspan="2">Method</th>
    <th colspan="4">General Capability</th>
    <th colspan="5">Specific Capability</th>
  </tr>
  <tr>
    <th>ARC-c</th>
    <th>GSM8K</th>
    <th>MMLU</th>
    <th>Avg.</th>
    <th>MGSM</th>
    <th>M3Exam</th>
    <th>XQuAD</th>
    <th>XLSum</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td rowspan="5">Llama3-8B</td>
    <td>Dense</td>
    <td>70.7</td>
    <td>58.3</td>
    <td>63.1</td>
    <td>64.1</td>
    <td>43.6</td>
    <td>55.1</td>
    <td>78.7</td>
    <td>49.1</td>
    <td>56.6</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>26.3</td>
    <td>2.5</td>
    <td>24.2</td>
    <td>17.7</td>
    <td>2.4</td>
    <td>23.6</td>
    <td>21.3</td>
    <td>32.8</td>
    <td>20.0</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>41.5</td>
    <td>0.0</td>
    <td>24.2</td>
    <td>21.9</td>
    <td>0.0</td>
    <td>17.4</td>
    <td>23.5</td>
    <td>8.3</td>
    <td>12.3</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>38.3</td>
    <td>0.0</td>
    <td>28.6</td>
    <td>22.3</td>
    <td>0.0</td>
    <td>28.3</td>
    <td>0.0</td>
    <td>3.1</td>
    <td>7.9</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>60.5</td>
    <td>25.7</td>
    <td>49.4</td>
    <td><b>45.2</b></td>
    <td>36.0</td>
    <td>44.7</td>
    <td>65.6</td>
    <td>46.3</td>
    <td><b>48.2</b></td>
  </tr>
  <tr>
    <td rowspan="5">Mistral-12B</td>
    <td>Dense</td>
    <td>82.6</td>
    <td>68.5</td>
    <td>50.4</td>
    <td>59.3</td>
    <td>53.2</td>
    <td>47.8</td>
    <td>62.2</td>
    <td>33.0</td>
    <td>49.1</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>22.5</td>
    <td>2.7</td>
    <td>30.7</td>
    <td>18.6</td>
    <td>2.8</td>
    <td>30.7</td>
    <td>31.8</td>
    <td>32.6</td>
    <td>24.5</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>49.4</td>
    <td>1.9</td>
    <td>32.1</td>
    <td>27.8</td>
    <td>1.6</td>
    <td>26.4</td>
    <td>28.3</td>
    <td>10.8</td>
    <td>16.8</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>37.8</td>
    <td>0.0</td>
    <td>33.9</td>
    <td>23.9</td>
    <td>4.4</td>
    <td>28.2</td>
    <td>29.1</td>
    <td>7.2</td>
    <td>17.2</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>68.3</td>
    <td>43.2</td>
    <td>39.5</td>
    <td><b>50.3</b></td>
    <td>38.4</td>
    <td>40.7</td>
    <td>50.6</td>
    <td>30.3</td>
    <td><b>40.0</b></td>
  </tr>
  <tr>
    <td rowspan="5">Llama2-13B</td>
    <td>Dense</td>
    <td>50.3</td>
    <td>31.4</td>
    <td>53.4</td>
    <td>45.1</td>
    <td>21.6</td>
    <td>36.5</td>
    <td>59.8</td>
    <td>35.3</td>
    <td>38.3</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>22.4</td>
    <td>2.1</td>
    <td>23.6</td>
    <td>16.0</td>
    <td>1.2</td>
    <td>23.3</td>
    <td>3.8</td>
    <td>25.1</td>
    <td>13.4</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>45.9</td>
    <td>2.4</td>
    <td>48.7</td>
    <td>32.3</td>
    <td>4.8</td>
    <td>24.5</td>
    <td>28.4</td>
    <td>11.2</td>
    <td>17.2</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>39.5</td>
    <td>3.8</td>
    <td>37.2</td>
    <td>26.8</td>
    <td>4.4</td>
    <td>22.9</td>
    <td>24.6</td>
    <td>13.7</td>
    <td>16.4</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>48.6</td>
    <td>20.7</td>
    <td>51.9</td>
    <td><b>40.4</b></td>
    <td>14.8</td>
    <td>28.2</td>
    <td>47.3</td>
    <td>34.4</td>
    <td><b>31.2</b></td>
  </tr>
  <tr>
    <td rowspan="5">Llama3-70B</td>
    <td>Dense</td>
    <td>84.1</td>
    <td>82.7</td>
    <td>78.8</td>
    <td>81.9</td>
    <td>68.4</td>
    <td>76.1</td>
    <td>81.3</td>
    <td>55.3</td>
    <td>70.3</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>69.1</td>
    <td>26.0</td>
    <td>53.2</td>
    <td>49.4</td>
    <td>16.8</td>
    <td>47.5</td>
    <td>56.1</td>
    <td>41.3</td>
    <td>40.4</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>65.7</td>
    <td>0.0</td>
    <td>54.2</td>
    <td>40.0</td>
    <td>6.4</td>
    <td>48.3</td>
    <td>42.2</td>
    <td>29.3</td>
    <td>31.6</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>59.4</td>
    <td>5.6</td>
    <td>75.5</td>
    <td>46.8</td>
    <td>12.4</td>
    <td>45.5</td>
    <td>44.6</td>
    <td>36.1</td>
    <td>34.7</td>
  </tr>
  <tr>
    <td><b>Cus-Prun</b></td>
    <td>72.3</td>
    <td>48.5</td>
    <td>65.2</td>
    <td><b>62.0</b></td>
    <td>40.8</td>
    <td>61.7</td>
    <td>66.9</td>
    <td>51.6</td>
    <td><b>55.3</b></td>
  </tr>
</table>
Table 7: Main Results of Cus–Prun on Thai with a pruning ratio of 25%, where “general capability” is tested in English and averaged across several expert models, while “specific capability” is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (%) for other datasets.

<table>
  <tr>
    <th rowspan="2">Model</th>
    <th rowspan="2">Method</th>
    <th colspan="4">General Capability</th>
    <th colspan="4">Specific Capability</th>
  </tr>
  <tr>
    <th>ARC-c</th>
    <th>GSM8K</th>
    <th>MMLU</th>
    <th>Avg.</th>
    <th>MGSM</th>
    <th>M3Exam</th>
    <th>XQuAD</th>
    <th>XLSum</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td rowspan="5">Llama3-8B</td>
    <td>Dense</td>
    <td>70.7</td>
    <td>58.3</td>
    <td>63.1</td>
    <td>64.1</td>
    <td>35.2</td>
    <td>43.0</td>
    <td>58.7</td>
    <td>16.7</td>
    <td>38.4</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>26.3</td>
    <td>2.5</td>
    <td>24.2</td>
    <td>17.7</td>
    <td>0.8</td>
    <td>24.4</td>
    <td>8.4</td>
    <td>13.5</td>
    <td>11.8</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>41.5</td>
    <td>0.0</td>
    <td>24.2</td>
    <td>21.9</td>
    <td>0.0</td>
    <td>12.3</td>
    <td>16.6</td>
    <td>8.7</td>
    <td>9.4</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>38.3</td>
    <td>0.0</td>
    <td>28.6</td>
    <td>22.3</td>
    <td>0.0</td>
    <td>25.4</td>
    <td>0.0</td>
    <td>2.3</td>
    <td>6.9</td>
  </tr>
  <tr>
    <td><b>Cus–Prun</b></td>
    <td>58.9</td>
    <td>31.2</td>
    <td>52.4</td>
    <td><b>47.5</b></td>
    <td>21.6</td>
    <td>38.3</td>
    <td>42.6</td>
    <td>16.8</td>
    <td><b>29.8</b></td>
  </tr>
  <tr>
    <td rowspan="5">Mistral-12B</td>
    <td>Dense</td>
    <td>82.6</td>
    <td>68.5</td>
    <td>50.4</td>
    <td>59.3</td>
    <td>45.2</td>
    <td>39.9</td>
    <td>44.1</td>
    <td>17.8</td>
    <td>36.8</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>22.5</td>
    <td>2.7</td>
    <td>30.7</td>
    <td>18.6</td>
    <td>1.2</td>
    <td>24.8</td>
    <td>11.9</td>
    <td>13.7</td>
    <td>12.9</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>49.4</td>
    <td>1.9</td>
    <td>32.1</td>
    <td>27.8</td>
    <td>0.0</td>
    <td>23.8</td>
    <td>8.4</td>
    <td>4.7</td>
    <td>12.3</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>39.5</td>
    <td>3.8</td>
    <td>37.2</td>
    <td>26.8</td>
    <td>0.8</td>
    <td>25.7</td>
    <td>4.7</td>
    <td>2.8</td>
    <td>8.5</td>
  </tr>
  <tr>
    <td><b>Cus–Prun</b></td>
    <td>68.2</td>
    <td>35.8</td>
    <td>47.6</td>
    <td><b>50.5</b></td>
    <td>32.8</td>
    <td>37.7</td>
    <td>35.6</td>
    <td>15.9</td>
    <td><b>30.5</b></td>
  </tr>
  <tr>
    <td rowspan="5">Llama2-13B</td>
    <td>Dense</td>
    <td>50.3</td>
    <td>31.4</td>
    <td>53.4</td>
    <td>45.1</td>
    <td>6.4</td>
    <td>24.3</td>
    <td>28.3</td>
    <td>14.5</td>
    <td>18.4</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>22.4</td>
    <td>2.1</td>
    <td>23.6</td>
    <td>16.0</td>
    <td>0.0</td>
    <td>22.3</td>
    <td>1.8</td>
    <td>10.2</td>
    <td>8.6</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>45.9</td>
    <td>2.4</td>
    <td>48.7</td>
    <td>32.3</td>
    <td>0.0</td>
    <td>26.2</td>
    <td>23.7</td>
    <td>8.6</td>
    <td>14.6</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>39.5</td>
    <td>3.8</td>
    <td>37.2</td>
    <td>26.8</td>
    <td>0.0</td>
    <td>23.1</td>
    <td>22.3</td>
    <td>8.9</td>
    <td>13.6</td>
  </tr>
  <tr>
    <td><b>Cus–Prun</b></td>
    <td>47.8</td>
    <td>20.9</td>
    <td>50.7</td>
    <td><b>39.8</b></td>
    <td>4.8</td>
    <td>24.2</td>
    <td>23.6</td>
    <td>13.8</td>
    <td><b>16.6</b></td>
  </tr>
  <tr>
    <td rowspan="5">Llama3-70B</td>
    <td>Dense</td>
    <td>84.1</td>
    <td>82.7</td>
    <td>78.8</td>
    <td>81.9</td>
    <td>65.2</td>
    <td>66.1</td>
    <td>67.8</td>
    <td>17.8</td>
    <td>54.2</td>
  </tr>
  <tr>
    <td>LLMPrun.</td>
    <td>69.1</td>
    <td>26.0</td>
    <td>53.2</td>
    <td>49.4</td>
    <td>15.6</td>
    <td>39.9</td>
    <td>29.8</td>
    <td>16.6</td>
    <td>25.5</td>
  </tr>
  <tr>
    <td>SliceGPT</td>
    <td>65.7</td>
    <td>0.0</td>
    <td>54.2</td>
    <td>40.0</td>
    <td>4.8</td>
    <td>41.3</td>
    <td>39.6</td>
    <td>13.2</td>
    <td>24.7</td>
  </tr>
  <tr>
    <td>ShortGPT</td>
    <td>59.4</td>
    <td>5.6</td>
    <td>75.5</td>
    <td>46.8</td>
    <td>13.7</td>
    <td>40.7</td>
    <td>40.4</td>
    <td>11.9</td>
    <td>26.7</td>
  </tr>
  <tr>
    <td><b>Cus–Prun</b></td>
    <td>73.3</td>
    <td>58.7</td>
    <td>68.4</td>
    <td><b>66.8</b></td>
    <td>40.4</td>
    <td>53.6</td>
    <td>58.5</td>
    <td>16.9</td>
    <td><b>42.4</b></td>
  </tr>
</table>

accuracy is the metric used for ARC-c, GSM8K, MMLU, MGSM, M3Exam, and XQuAD, while Rouge-L (Lin, 2004) is used for XLSum.

A.3.2 Multidomain Settings

Settings For multidomain setting, we can obtain two corpora: \( C_{D_{\text{Exp}}} = \{(L, D_{\text{Exp}}, T)|L \in \mathbb{L}, T \in \mathbb{T}\} \) and \( C_{(L,T)_{\text{Exp}}} = \{(D, (L, T)_{\text{Exp}})|D \in \mathbb{D}\} \). The first corpus contains samples in a specific domain across various languages and tasks, while the second corpus contains samples from a specific language-task combination across different domains, i.e., the target dataset in other domains. Specifically, for \( C_{D_{\text{Exp}}} \) we employ specific domain corpus, including English split of medical corpus (García-Ferrero et al., 2024) for medical domain, general finance corpus for finance domain\footnote{https://huggingface.co/datasets/gbharti/finance-alpaca}, general Twitter corpus (Kharde and Sonawane, 2016), and English split of Amazon corpus (Keung et al., 2020). For \( C_{(L,T)_{\text{Exp}}} \), we employ the corresponding datasets in general domains, including CommonsenseQA (Talmor et al., 2019) for MedMCQ, open table question-answering OTT-QA (Chen et al., 2020) for FinTQA, general sentiment analysis (Attia et al., 2018) for TSA and AMSA.

Experiment Details Hyperparameters, including the sizes of \( C_{D_{\text{Exp}}} \) and \( C_{(L,T)_{\text{Exp}}} \), are determined using the validation set of the Amazon sentiment analysis dataset and then applied to testsets in other multidomain datasets. Furthermore, accuracy is the metric used for all datasets.

A.3.3 Multitask Settings

Settings For multitask setting, we can obtain two corpora: \( C_{T_{\text{Exp}}} = \{(L, D, T_{\text{Exp}})|L \in \mathbb{L}, D \in \mathbb{D}\} \) and \( C_{(L,D)_{\text{Exp}}} = \{(T, (L, S)_{\text{Exp}})|T \in \mathbb{T}\} \). The first corpus contains samples in a specific task across various languages and domains, while the second corpus contains samples from a specific language-domain combination across different tasks, i.e., the target dataset in other tasks. Specifically, for \( C_{T_{\text{Exp}}} \) we employ specific task corpus, including XSum corpus (Abacha and Demner-Fushman, 2019) for summarization task, general counterfactual corpus\footnote{https://huggingface.co/datasets/azhx/counterfact-easy} for counterfactual task. For \( C_{(L,D)_{\text{Exp}}} \), we employ the corresponding datasets in other tasks, including MedQCQ (Pal et al., 2022) for MedSum, AMSA (Zhang et al., 2015) for AMSum and AM-ContFact.

Experiment Details Hyperparameters, including the sizes of \( C_{T_{\text{Exp}}} \) and \( C_{(L,D)_{\text{Exp}}} \), are determined us-
ing the validation set of the Amazon counterfactual dataset and then applied to testsets in other multitask setting datasets. Furthermore, accuracy is the metric used for ARC-c, GSM8K, MMLU, and AMContFact, while Rouge-L (Lin, 2004) is used for MedSum and AMSum.

Reviewer paper summaries: The paper proposes Cus-Prun, a method to prune general Large Language Models (LLMs) into specialized expert models by selectively pruning neurons along "language", "domain", and "task" dimensions without post-training. It achieves strong performance on specialized tasks, maintaining minimal degradation in general capabilities.
Summary of strengths: 1. The paper is well-written and easy to follow.
2. The method offers surpasses coarse-grained methods in the proposed specific scenarios without retraining.
3. Comprehensive evaluations across multiple languages, domains, and tasks confirm consistent improvements over baselines.
Summary of weaknesses: 1. Lack of Novelty. Pruning based on specific dimensions (language, domain, task) [1] is a common and widely studied concept, making the contribution incremental.

   [1] Pruning as a Domain-specific LLM Extractor.

2. Limited Sparsity Analysis. The paper lacks comprehensive analysis across different sparsity ratios. Experimental results presented only focus on a fixed 25% sparsity ratio, limiting the understanding of method robustness under varying levels of sparsity.

3. Experimental Design Issue. The proposed method appears more like a paradigm potentially integrable with other pruning methods. However, the experimental comparison primarily uses SliceGPT as the base method while comparing with other unrelated pruning methods (ShortGPT, LLMPrun), instead of systematically pairing Cus-Prun with multiple base pruning methods to assess its general applicability and incremental improvements.
Comments, suggestions and typos: None.
Overall assessment scores: 2.5
Metareview score: 3
Metareview: This paper introduces Custom Pruning (Cus-Prun), a novel method for pruning large general-purpose language models (LLMs) into smaller, specialized expert models without requiring post-training. The method targets specialization along three dimensions: language, domain, and task, by identifying and removing neurons deemed irrelevant to the desired expert profile based on activation patterns observed with dimension-specific corpora. The authors demonstrate Cus-Prun's effectiveness across various LLMs (Mistral, Llama2, Llama3) and settings, showing it can outperform other pruning techniques like SliceGPT, ShortGPT, and LLMPruner in maintaining both expert and general capabilities, even at significant pruning ratios.

Reviewers generally acknowledged the practical relevance of creating specialized, lightweight models without post-training and the innovative focus on customization along defined dimensions. The comprehensive experimental evaluation across multiple models, languages, domains, and tasks was also noted as a strength.   

However, several concerns were raised. Reviewer oy3y questioned the novelty compared to existing dimension-specific pruning work and noted the analysis was limited to a fixed sparsity ratio initially. Reviewer Vysa questioned the justification for selecting only three dimensions (language, domain, task)  and pointed out a lack of explicit discussion on model size reduction metrics like parameter counts or memory footprint, despite this being part of the motivation. Reviewer ALzs raised concerns about the simplicity of the neuron importance assessment (relying mainly on activation patterns), the lack of a method to determine optimal pruning ratios automatically, and the static nature of the pruned model. Reviewer sWHW requested clarification on the precise definition of a "neuron" in the context of structured pruning and pointed out the need for hardware/runtime details for speedup claims. The fairness of comparison with methods like LLM-Pruner, which aims for general capability preservation, was also questioned.

The authors provided detailed rebuttals, addressing many of these points. They distinguished their approach from prior work by highlighting its broader applicability and gradient-free nature. They clarified that higher sparsity ratios were indeed explored (Table 3)  and that model size/speedup details were provided, promising to enhance clarity. They justified the choice of dimensions as a practical starting point aligned with common model categorization and explained why pruning for finer-grained attributes like style might be more complex. They also clarified that evaluations across combined dimensions (e.g., domain+language) were performed (Section 5.1). The definition of a "neuron" (row/column in parameter matrices) was provided, along with hardware details and an additional comparison showing Cus-Prun outperforming LLM-Pruner even when the latter used specialized calibration data (without fine-tuning). They acknowledged that dynamic pruning and comparisons with distillation (which requires extensive retraining ) are potential future work.
Summary of reasons to publish: 1) Addresses the important practical problem of creating efficient, specialized LLMs from general ones without costly post-training.
2) Proposes a novel, structured approach (Cus-Prun) based on language, domain, and task dimensions. 
3) Demonstrates strong empirical results across diverse models and scenarios, outperforming several relevant baselines.
4) Authors provided substantial clarifications and additional experimental details in response to reviewer concerns, strengthening the paper's claims.