Neuron-Aware Data Selection in Instruction Tuning for Large Language Models
Authors: Xin Chen
Junchao Wu
Shu Yang
Runzhe Zhan
Zeyu Wu
Min Yang
Shujian Huang
Lidia S. Chao
Derek F. Wong
Venue: ICLR 2026 Poster (2026)
Keywords: Instruction Tuning
Data Selection
Large Language Models
Primary area: interpretability and explainable AI
TL;DR: NAIT is an efficient algorithm that selects high-quality instruction tuning data by analyzing neuron activation pattern similarity, enhancing large language models' performance and general capabilities.

Abstract: Instruction Tuning (IT) has been proven to be an effective approach to unlock the powerful capabilities of large language models (LLMs). 
Recent studies indicate that excessive IT data can degrade LLMs performance, while carefully selecting a small subset of high-quality IT data can significantly enhance their capabilities. Therefore, identifying the most efficient subset data from the IT dataset to effectively develop either specific or general abilities in LLMs has become a critical challenge.
To address this, we propose a novel and efficient framework called Nait. Nait evaluates the impact of IT data on LLMs performance by analyzing the similarity of neuron activation patterns between the IT dataset and the target domain capability. Specifically, Nait captures neuron activation patterns from in-domain datasets of target domain capabilities to construct reusable and transferable neuron activation features. It then evaluates and selects optimal samples based on the similarity between candidate samples and the expected activation features of the target capabilities.
Experimental results show that training on the 10\% Alpaca-GPT4 IT data subset selected by Nait consistently outperforms methods that rely on external advanced models or uncertainty-based features across various tasks. Our findings also reveal the transferability of neuron activation features across different capabilities of LLMs. In particular, IT data with more logical reasoning and programmatic features possesses strong general transferability, enabling models to develop stronger capabilities across multiple tasks, while a stable core subset of data is sufficient to consistently activate fundamental model capabilities and universally improve performance across diverse tasks.

Published as a conference paper at ICLR 2026
NEURON-AWARE DATA SELECTION IN INSTRUCTION TUNING FOR LARGE LANGUAGE MODELS
Xin Chen 125,,, Junchao Wu'r", Shu Yang', Runzhe Zhan', Zeyu Wu', Min Yang*At, Shujian Huang', Lidia S. Chao', Derek F. Wong
CLP Lab, Department of Computer and Information Science, University of Macau,
2 Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,
*Provable Responsible Al and Data Analyties Lab, KAUST,
'National Key Laboratory for Novel Software Technology, Nanjing University
nlp2ct fxinchen, junchao, runzhe, 2eyull com, min-yangesiat.ac ac. shu. yangekastt.. edu. sa, {derekfw, iiii,,iiii lidiasc] num.edu.m
ABSTRACT
Instruction Tuning (IT) has been proven to be an effective approach to unlock the powerful capabilities of large language models (LLMs). Recent studies indicate that excessive IT data degrade LLMs performance, while carefully selecting a small subset of high-quality IT data can significantly enhance their capabilities. Therefore, identifying the most efficient subset data from the IT dataset effec- tively develop either specific or general abilities in LLMs has become a critical challenge. To address this, we propose novel and efficient framework called NAIT. NAIT evaluates the impact of IT data on LLMs performance by analyzing the similarity of neuron activation patterns between the IT dataset and the target domain capability. Specifically, NAIT captures neuron activation patterns from in-domain datasets of target domain capabilities to construct reusable and trans- ferable neuron activation features. It then evaluates and selects optimal samples based on the similarity between candidate samples and the expected activation fea- tures of the target capabilities. Experimental results show that training on the 10% Alpaca-GPTI IT data subset selected by NAIT consistently outperforms methods that rely external advanced models uncertainty- -based features across vari- ous tasks. Our findings also reveal the transferability of neuron activation features across different capabilities of LLMs. In particular, IT data with more logical reasoning and programmatic features possesses strong general transferability, en- abling models to develop stronger capabilities across multiple tasks, while a stable core subset of data is sufficient to consistently activate fundamental model capa- bilities and universally improve performance across diverse tasks.
INTRODUCTION
IT of LLMs has become a foundational technique for activating LLMs instruction and knowledge capabilities (Ouyang et al.) 2022). Previous studies have shown that excessive IT data can degrade LLMs performance, while selecting small amount of high-quality IT data can significantly improve model performance. For instance, LIMA (Zhou et al. (023) achieved impressive results using only 1k IT data. However, major challenge remains as current approaches lack interpretability in iden- tifying "high-quality" data and fail to enhance the specific (one more) target domain capabilities of LLMS in an open dataset (Wu et al., 2023) Qin et al.) 2024 Waag et al. (024a)). Moreover, ex- isting SOTA IT data selection methods like Instruction Mining (Cao et al.) 2023). AlpaGasus (Chen) ctt al. 2024) and SelectIT (Liu et al.; (024b) often rely on surface-level features, external models and data for scoring, the model's uncertainty, which are computationally expensive. This limits the scalability of these methods and their broader application to large-scale data.
Equual contribution.
Corresponding author.

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Published as a conference paper at ICLR 2026
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(4) Neuron Activation Feature Extraction for Target Capability
(b) Activation Feature Guided Data Selection..
Figure 1: Overall framework of NAIT. First, we capture the neuron activation of the LLM using in-domain data that we want the model to learn. Next, we construct activation feature through a dimensionality reduction method. Finally, we evaluate the feature alignment score between this activation feature and the model's activation each candidate dataset to guide data selection.
Inspired by previous works on neuron activations and capabilities of LLMs (Voita et al. 2024 Xu et al., 2024c), we address these concerns in NAIT (Neuronal Activation- efficient IT data se- lection framework), a novel approach designed evaluate data quality and enhance specific domain capabilities of LLMs by analyzing neuronal activation patterns related to the target domain capabili- ties of the LLM. The NAIT framework built on the core hypothesis that when an LLM processes a given sample, the closer the model's resulting neuronal activation pattern aligns with the activation pattern of the target capability, the more effectively that sample enhances the LLM's performance in that specific domain.
NAIT operates in two main stages. First, we extract neuron activation features corresponding to the LLM's target capability. This process begins by providing a small set of in-domain examples that exemplify the specific capability of interest. As the LLM processes these examples, its neuron activation states are recorded identify reusable activation features linked the desired capability. These features serve as representation of the LLM's response and neural activation preferences for the target tasks, forming the foundation for the subsequent data selection phase. Next, we perform capability-driver data selection by identifying and selecting IT data that can further activate and enhance the model's relevant capabilities. This is achieved by comparing the neuron activation fea- tures of candidate data within an open and diverse dataset (e.g., (PPaca-GPP4) against the in-domain activation features. By prioritizing data that closely matches the neuron activation patterns of the target capability, NAIT ensures that the selected data is both relevant and effective for improving the model's specialized abilities. In general, NAIT exhibits the following properties: (1) Efficiency ($4.2) & $5.1& & $5.2 Appendix(H), NAIT achieves strong performance with only small number of in-domain examples and IT data, eliminating the need for external models. It also boasts low computational cost and minimal time requirements. (2) Robustness ($5.1): The framework demon- strates excellent adaptability across different base models, IT datasets, and domain-specific datasets. (3) Interpretability ($5.3): Qualitative analysis shows that NAIT unveils the logical reasoning and programmatic features that possess strong general transferability. Additionally, NAIT identifies a subset of core IT data that remains stable across different task-specific activation features.
Experimental results demonstrate that training LLaMA-2-7b on only 10% of the Alpaca-GPT4 IT dataset selected using NAIT yields an average performance improvement of 3.24% across five tasks compared to training on the entire dataset using IT. Further experiments show that NAIT consis- tently improves performance across different numbers of in-domain data, base models, diverse IT datasets and selection strategies, highlighting NAIT's strong robustness. The method based on neu- ron activation features also shows strong interpretability. Further analysis reveals that NAIT tends to select IT data with more logical reasoning and programmatic neuron activation features. Such data can effectively enhance the model's reasoning ability and general ability across various downstream tasks. The key contributions of this paper
follows:
Proposing NAIT, the first instruction data selection framework based on neuron activation pat- temss, introducing a new paradigm for the targeted development model capabilities.
Revealing the correlation between neuron activation features and the retention of fundamental model capabilities, while exploring the transferability of these features across different down- stream tasks.
Open-sourcing a cross-task neuron feature library and the Alpaca-NAIT dataset, a high-quality IT dataset curated from the Alpaca-GPPi dataset using our proposed NAIT.
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Table 1: Comparison of our method and existing methods. indicates the presence of feature,
X indicates its absence, and O denotes partial support.
Frature Starce
Anternall-Inddeeeendd Gradient-Frre
Targrted Ability
Cont-illrctin
Interprstbiility
LIMA
Zhou ctal.
2023
Human
X
Inscuation
X
AlpuCiasp
Clan
X044
ChiGPP Soot:
x
SelectT
et
Mult gramuarr Uncertainty
LESS
Goalicn-heod
X
X
NATT (Utes)
Neuma Actication
RELATED WORK
IT plays a crucial role in bridging general pretraining and task-specific alignment, as summarized in Appendix C
It enables LLMs to follow instructions, activate latent abilities, and significantly enhance downstream task performance.
2.1 HIGH- QUALITY IT DATA
Recent studies have highlighted the key role of high-quality IT data in enhancing the alignment of LLMs with human preferences as well as generating accurate, relevant, and safe responses (Wang et al., 2024a| Liu et al., 2024a). Typically, IT data consists of instruction-response pairs, which can be created in two main ways: (1) by reformulating traditional NLP task data into the IT for- mat (e.g., FLAN (Wei al. 2022) and P3 (Sanh et al.] (022)) (2) by synthesizing new data using self-instruction based on a small set of manually crafted seed IT data (e.g.. Alpaca (Taor] ct al. ()23)). Subsequent work has further explored methods for synthesizing high-quality IT data. For example, Vicuna (Chiang et al.
2023) enhanced data diversity with large-scale dialogue data, while WizardLM (Xu al.J (0244)) used LLMs automatically generate IT datasets with control- lable complexity. Notably, LIMA (Zhou et al. (2023) demonstrated that LLMs fine-tuned on only IK carefully curated IT data can achieve comparable performance of models trained with large- scale dataset, highlighting the importance of data quality over quantity. These findings suggest that pretrained I.MMs already contain extensive world knowledge and require only a small amount of high-quality instruction data to yield strong performance in the IT phase.
However, defining what constitutes "high-quality" IT data remains challenging. Data quality is influenced by complex, multidimensional factors, and there is still a lack of interpretable selection mechanisms and standardized criteria (Wang et al. 2024a).
2.2 EFFICIENT IT DATA SELECTION
Existing IT data selection strategies typically filter subsets based on metrics such quality, diversity, or importance (Qin et al. 2024). These strategies fall into four main categories based on how these metrics are extracted: (1) Handcrafted Feature- -aased Methods rely on manually designed features. For example, DQI (Mishra et al.) 2020) selects high-quality data by leveraging lexical features, n-gram frequency, and relational features, While et al. (2023) optimize data distribution using æ-gram features. Although these methods more interpretable, they are limited to surface- level characteristies and cannot capture deeper data properties; (2) Model Feature-Based Methods extract features such as uncertainty or perplexity from model outputs. For instance, Instruction Mining (Cao al., 2023) leverages perplexity and reward scores, and SelectIT (Liu al. 2024b) conducts multi coolti-grauulle gramularity uncertainty analysis for data selection. However, these methods depend heavily on model outputs, which may introduce bias, and often exhibit "black-box" characteristics that limit interpretability.
(3) LL.M-as-Scorer Methods utilize advanced LLMSs, such as ChatGPT, to score data based on complex criteria. For instance, InsTag (Lu et al., 2024) evaluates diversity and complexity in IT data, while AlpaGasus (Chen et al. 2024) scores data quality similarly. These approaches, though effective, are computationally expensive, lack transparency, and are difficult to control or scale due to reliance closed-source APIs. (4) Loss and Gradient-barl based Coreset Sampling Methodsselect data based on loss values or gradient information. For example, Chen et al.) 2023) select samples minimize evaluation loss, and Xia et al. (2024) gradient signals to
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identify critical data points. These methods computationally intensive, especially for large-scale models, and may suffer from approximation errors.
In summary, existing data selection methods often require substantial computational resources, ad- ditional model training, or expensive queries, which limit their scalability to large datasets. Further- more, they may struggle to enhance specific target domain capabilities of LLMs when applied to open datasets.
3 OUR NAIT METHOD
3.1 PRELIMINARY
Neuron activation analysis provides insights into the knowledge storage and operational mechanisms
of LLMs (Voita et al.) 2024] Durrani et al.) 2020 Xu et al. (202c)). Recent studies show that subsets of neurons are activated by specific tasks, playing a critical role in LLMs' ability process
knowledge and solve various tasks Yuu & Ananiadou, 2024 Wang et al. 2024b Tang et al. 2024).
Motivation Inspired by these findings, we hypothesize that the effectiveness of instruction data may lie in its ability to activate task-relevant neurons. Specifically, when an LLM processes a given sample, the closer the neuron activation pattern aligns with the activation characteristics the target capability, the more effective the sample is in improving the LLM's performance in that domain.
Comparison Compared to existing methods, the proposed NAIT approach offers significant ad- vantages, shown in Table [ Specifically, NAIT directly leverages neuron activation patterns within LLMs, eliminating the need for external models or complex proxy features and greatly im- proving data selection efficiency. Furthermore, due to the strong correlation between neuron activa- tion and the internal knowledge and tasks of LLMs, NAIT can effectively improve both the model's specialized and general domain capabilities. By capturing the internal neuron activation states dur- ing LLM operation, NAIT also provides robust interpretability for data selection. Overall, NAIT outperforms existing methods in efficiency, scalability, and interpretability, offering more effective solution for IT data selection.
3.2 OUR DATA SELECTION FRAMEWORK
NAIT identifies IT data that effectively enhances specific domain capabilities C in the LLM M via two main modules: (a) Neuron Activation Feature Extraction and (b) Activation Seature-Guider Data Selection, as illustrated in Figure T
The proposed framework is detailed below (the detailed
algorithm is provided in Appendix
3.2.1 MODULE (A). NEURON ACTIVATION FEATURE EXTRACTION FOR TARGET CAPABILITIES
To establish the neuron activation features associated with target capabilities C, we implement two-stage features extraction process:
In-domain Dataset and Activation Capture
We aim analyze the intrinsic representations of capabilities C by constructing representative
dataset P = {P.). Given an in-domain sample P: = (41....,44 (x) from P, we record the activa- tions in model M across the decoder layers L. For specific layer and token tg, the activation vector is defined as:
A(tx) = |a 1/
where J denotes the number of neurons in the layer. Therefore, its relative change AA(te) with respect to the beginning token b can defined as:
AA (tx))
where AA(tz) J quantifies the dynamic activation shifts for each neuron throughout the generation process. To summarize the activations for the entire sequence, we compute the mean activation across all K tokens.
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Neuron Activation Direction Extraction To obtain the characteristic direction vectors V, apply Principal Component Analysis (PCA)
to the difference set A, For each layer 1, extract the first principal component:
PCA(AA())
To ensure the direction Vi aligns with the capability's activation trend, compute the mean difference Jairr [P| (A (4)).. We calibrate the direction as follows:
Vi 5 { [-i
if Pain <
(4)
Vi
otherwise
The final set of direction vectors is V {vi)-
3.2.2 MODULE (B). ACTIVATION FEATURE-GUIDED GUIDED DATA SELECTION
Given the instruction dataset Dins, we perform data selection based on the alignment with the ex- tracted directions V.
Activation Feature-guided Data Scoring
For each sample Dins, we calculate a score sy by projecting the sample's activation onto the target direction Vi and summing across all layers
8y (4
This score Sy measures how strongly the sample activates the specific neurons associated with the target capability.
Data Ranking & Selection Finally, we select the subset Dellcttd containing the top-k samples with the highest scores:
D Deelectd -to-k((S)
(6)
EXPERIMENTS
4.1 SETUPS
Benchmark We evaluate NAIT acTOSs five representative capability domains using widely adopted benchmarks: factual knowledge with MMLU (Hendrycks al. (02T)) and MMLU-Pro [Wagg et al.) 2024c). mathematical reasoning with GSM (Cobbe et al. 2021) and SVAMP (Patel al. 2021), general reasoning with BBH (Suzgun et al. (023), multilingual understanding with Ty- DiQA (Clark et al. 2020) and XQuAD (Artetxe et al. 2019), and coding ability with BigCodeBench (Zhuo et al. 2024), HumanEval (H-Eval) (Chen et al. 2021) and MBPP (Austin et al. 2021). We in-domain splits capability references and conduct evaluation the corresponding test sets. Benchmark setups are provided in Appendix D
Baselines We compare NAIT against diverse set of representative IT data construction and selec- tion approaches: Alpaca-GPT4 (Peng et al. (023), widely used self-instruct dataset synthesized by GPT-4; LIMA (Zhou al. 2023), which shows that small amount of carefully curated high-quality IT data can be highly effective; AlpaGasus (Chen et al. 2024). which leverages ChatGPT to score and filter data; Q2Q (Li et al. (222), which evaluates data quality via loss signals from precursor model; and SelectlT (Liu et al. 2024b), which selects high-quality data by exploiting uncertainty estimates from base L.LMs. Detailed descriptions are provided in Appendix [D] Additionally, we compare our method with the targeted ability activation approaches, including embedding-basee methods, representation- al. 2024). as detailed in Appendix
(ZahhoddsZhhng et al. 2018: Hanawa et al. 2021), and LESS (Xia
Implementation Details In this study, we adopt LLaMA-2-78 7b as the foundational model for fine- tuning. The detailed fine-tuning and text generation settings provided in Appendix
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Table 2: Performance comparison of baslines using 10% of the IT data. This table shows the results of various tasks across multiple baselines. NAIT (e.g., NAIT (MMLU) refers to the process where in-domain dataset to guide the IT data selection. Random refers to a randomly sampled 10% subset of the IT data. The Bold and Underline represent the best and second performance (t) indicates the performance improvement relative to the ID 01.
respectively in each column.
System ID
Method.
Kanelsige MMLU
Fatull
Reseering GSM
Mathonatical
Casling Aldlity
Maltilinuul Undercaraling NQuAD
Ganen Reaauning BBH
MMLU-Po
SVAMP
H-Eval
MBPP
A(1)
Full Fine-Jating
01
Prnc al M003
46.87
11.89
14.63
90.00
22.87
51.58
19.45
42.99
19.94
36.04
LIMA
45.20
25.04
15.76
37.67
27.25
46.36
46.92
44.72
39.91
36.17
+0.39%
43.21
21.96
36.67
46.08
35.18
-2.34%
04
01 Q2Q
L3
1023
45.73
21.50
14.50
35.00
25.19
44.97
46.41
48.41
40.34
35.68
0.96%
Sclecll
43.158
Random Resclins
31.4%
35.67
47.35
44.15
30.21
-0.448
Our Mi
Milhod (istiiddal Cajiability Peunirer)
08
O1 + NAIT (GSS)
45.45
24.50
16.00
41.53
27.81
48.41
46.51
47.97
40.25
37,70
+4.65%
09
01 NAIT (CaleX)
47.51
33.46
15.53
38.67
28.49
19.74
44.19
46.27
39.72
37.06
42.88%
(TyliQA)
46.17
11
01 + NAIT (BBH)
47.78
23.36
13.34
36.67
25.15
47.08
45.93
48.46
40.46
36.47
+3.23%
Our Propoood Marlaed (Aii Copurelity Fratuns)
01 NA
46.83
23.29
39.67
47.62
38.21
20.02
37.30
'.3.346
4.2
MAIN RESULTS
Overall Performance The NAIT method effectively enhances model's overall performance by leveraging neuron activation features across multiple domain capabilities. As shown in Table Sys- tem represents the baseline performance of the Alpaca-GPT4, while System 12 demonstrates the proposed NAIT method surpassing Alpaca-GPT4 GPT4 under full fine-tuning setting. Notably, it achieves up to a 4.65% % improvement using mathematical reasoning features (System 08) and 3.24% im- provement when combining all capability features (System 12). These results significantly surpass existing IT data selection methods, such as AlpaGasus (System 03), Q2Q (System 04), and SelectIT (System 05), while utilizing only 10% of the original data. We also observe that activating specific domain capabilities influences downstream tasks differently. For instance, utilizzig only the mul- tilingual capability (System 10) leads to decreased performance on mathematical and coding tasks compared to the baseline. In contrast, when all in-domain capabilities are activated (System 12), the model's mathematical performance notably improves from 14.64 to 16.53, demonstrating the robustness of NAIT.
NAIT with Distinct Capability Features The experimental results from System 07 to 11 demon- strate that the NAIT method can achieve more consistent and stable performance improvements when targeting individual-specift abilities. Notably, this advantage is particularly pronounced in complex tasks such as mathematical reasoning. multilingual understanding, and coding ability, with improvements of 7.53% and 6.29%, and 5.33 % over the random baseline, respectively. Further analysis reveals that the contributions of neuron activation features vary across different task do- mains and exhibit certain degree of cross- cross-domain transferability. For instance, neuron activation features extracted based on the MMLU in-domain dataset can significantly enhance the model's per- formance on the multilingual understanding task, while those extracted from the CodeX in-domain dataset can likewise effectively improve the model's performance on the GSM task. It is worth men- tioning that the performance of the NAIT method on the GSM dataset reaches as high as 4.65%, even surpassing the model under multi-capabille pare our method with targeted ability activation approaches, including embedding based methods, representation-basee methods (Zhang et al. [018]] Hanawa et al., 2021). and LESS (Xia et al.. 2024), as detailed in Appendix D Overall, NAIT consistently achieves the superior overall average perfor- mance across all targeted settings. While gradient-base methods like LESS can outperform in the specific target domain to some extent, this specialization often comes at the cost of generaliza- tion. leading to performance degradation in non- target tasks. In contrast, NAIT demonstrates robust transferability. maintaining high proficiency across multilingual and reasoning tasks regardless of the target feature.
activation (System 12). Additionally, we com-

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Published as a conference paper at ICLR 2026
5 ANALYSIS
This section aims to analyze further and address the following research questions: (1) How do the number of in-domain datasets, the proportion of IT datasets, model size, and IT data methods affect NAIT? (see $ I.I (2) What are the advantages of NAIT in cost efficiency? (see $ 55.2) (3) How interpretable in NAIT? (see $ (5.) (4) How does NAIT perform in target domain? ($ Appendix H)
5.1 ABLATION STUDY
Proportion of Selected IT Dataset Although NAIT demonstrates excellent performance in data evaluation and ranking, selecting the optimal data proportion from IT datasets with a large amount of redundant information remains challenging. To further investigate this, we adjusted the proportion of the selected IT dataset from 10% to 100% and analyzed the performance of NAIT under different IT data proportions. As shown in Figure 2 the top 30% of IT samples selected by NAIT exhibited the best performance. Notably, the proportion of training data continued to increase, the model's performance showed an overall downward trend, reaching its lowest point when the entire dataset (100%) was used. These results indicate that excessive redundant data may undermine the model's generalization, further emphasizing the need for data selection to maximize model performance.
Number of In-domain Data The number of in-domain data is critical parameter in our method, as it directly influences the effective- ness of extracting neuron activation features for specific domain capabilities. As shown in Fig- ure 3 we fine-tune the model using IT data se- lected based on the activation patterns of each in-domain data group and evaluate its perfor- mance corresponding tasks. The results in- dicate that NAIT outperforms the random se- lection baseline across all tasks in most cases. Even when the data size is small (e.g., 16, 64, or 256 samples), most cases exceed the ran- dom baseline. This suggests that even with limited data, the extracted features effectively capture the required capabilities. As the num- ber of in-domain data increases, performance improvements gradually level off, with most tasks reaching their maximum performance at the largest data size. However, for GSM and TydiQA, peak performance achieved at 18.35 and 47.57, respectively, when the data size is 4096. This highlights that the quality of in-domain data also plays important role in enabling NAIT to capture task-specific features.
50
Polessa
20
10
0.1
0.2
0.3
3.4
0.5
0.5
0.7
0.5
0.9
1.0
SHART 2004 Teepp www.nitttti...o (eagge
Goldt SALTDOARO Competoniio NOTT/W
Figure 2: Performance at different proportions of the IT dataset. Task performance NAIT (the in-domain dataset), e.g., MMLU in NAIT, refers to using in-domain data to guide IT data selection and to assess task outcomes. The comprehensive results correspond to System 12.
Intensity of Neuron Activation We evaluate the effectiveness of NAIT by comparing three NAIT settings: Random (10% samples ran- domly selected). High (top 10% with high- est neuron activation feature alignment scores), and Low (10% with lowest neuron activation feature alignment scores). As shown in Table 3 Specifically, NAIT with highest neuron ac- tivation feature alignment scores outperforms the Random baseline by 3.35%, indicating that these samples effectively enhance the models' capabilities. In contrast, NAIT with lowest neuron activation feature alignment scores reduces performance by 17.54% compared to Random baseline, suggesting that such samples not only fail to help but also negatively impact the LLM's effectiveness.
Table 3: Performance comparison across datasets at 10% IT dataset under different neu- ron activation levels.
Overall
Intensity
MMLU
BRH
H-Esal
TydiQA
H-Eval
AVG
Art)
Rondom High Low
47.14
39:21
14.13
25.55
34.04
46.83
40.02
16.33
46.09
26.44
35.18
+3.35%
46.30
28.15
10.61
36.12
20.15
28.27
-17.5%%
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1140
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Figure 3: Comparison of NAIT with random data selection across different in-domain data scales at 30% IT dataset. Task performance in NAIT (in-domain dataset), such as MMLU in NAIT (MMLU), refers to using the in-domain dataset to guide IT data selection and evaluate task performance. The dashed line indicates the 30% of the IT dataset random selection baseline.
Various Foundation Models Our method demon- strates significant performance improvements when fine-tuning the LLaMA-2-7b model with the curated Alpaca-NAIT dataset. To evaluate its robustness and generalizability, we extend our study to other foun- dational models, including (1) LJaMA-2-13b repre- senting models of different scales; and (2) Mistral- 7b and LLaMA-3-8b, representing models with dif- ferent architectures, and (3) Qwen-2.5-7b, represent- ing state-of thetter models with stronger baselines. As shown in Table 4 NAIT consistently enhances performance across all evaluated models. Specif- ically, it maintains effectiveness on larger scales (LLaMA-2-13b) and achieves substantial gains of
Table 4: Effectiveness of NAIT Across Models. Alpaca and Random trained on the full dataset and a random of 10% subset, while NAIT selects a 10% subset.
Method
MMLU
BBU
GSM
TydiQA
I-Eaall
Overall
AVG
A(11
ELama 2.1
Alguci
RRndom
$2.70
47.59
22.06
44.66
36.79
40.76
+2.57%
.NATT
54.30
47.04
24.11
45,89
38.53
42.53
47,02%
Micrui-76
Kipuci
35.00"
70.19
12.89
"3,000
"3.96"
Random
48.37
46.11
17.36
$4.90
4181
57.69
+10.26
EAasoo 5-4b
Aipaci
34.60
14.30
26. T3"
28,56
51.36
"49.3"
+21. 1..% on Mistral-7b and +18.65% LLaMA-3-
Raankom
55.93
54.65
-7.27%
NNTT
60.50
60.93
43.91
47.72
74.78
57.63
+18.65%
8b. Notably, even on the highly capable Qwen-2.5- 7b, NAIT further boosts the average performance by +3.83% outperforming the random baseline and val- idating its efficacy regardless of the model's initial capabilities.
(hron-) 5-7h
Aljuas
78.30
R5.46
85.11 17
46.53
90.85
"12.42"
RRandmm
34.00
87.78
84.31
51.25
89.63
73.19
+1.34%
NNAIT
74,21
67.87
83.70
58.14
92.07
75,20
+3.3%%
Different Instruction Tuning Datasets To further verify the effectivenesss of our method, we extend our IT selection method to other IT datasets, in-
Ewil-hemiss
cluding Evo-Instruct (Xu et al. 2024a) using Wiz-
Pefommnae
40
36.88
37.76
ardLM and Orca-GPT4 (Mukherjee et al. 2023). The experimental results are shown in Figure 4 as the proportion of IT data increases, the model per-
formance rises and then declines. Notably, the effi-
cient IT data subsets selected by NAIT achieve op- timal performance at 50% (Orca-GPT4) and 80% (Evo-Instruct), significantly outperforming the use of the full dataset. Additionally, Evol-Instruct and Orca-GPT4 require higher proportion of data, es- pecially Evol-Instruct, which needs up to 80% to achieve optimal performance, reflecting their higher quality, complexity, and information density com- pared to Alpaca-GPT4.
30
Proputtonn of Datooa
Figure 4: Comparison of NAIT across dif- ferent IT dataset. Task performance in NAIT refers to using the all in-domain dataset to guide IT data selection and evaluate task performance.
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Different Selection Strategies We also conducted experiments using context length and perplex- ity as measures of data complexity, following Liu et al. (2024c), to provide an additional insight into method's effectiivness, detailed in Appendix
5.2 COST EFFICIENCY
We compare inference time and cost between NAIT and existing approaches, including Al- paGasus (GPT-40)), Q2Q (GPT-40), and Selec- IT, on the Alpaca-GPP shown in Table NAIT achieves up 19x and 4x cost reductions compared to AlpaGasus and Q2Q, respectively, along with significant improvements in inference speed by 17.75 and 2.2 hours. Compared Select|T, NAIT reduces cost by 94.3% and achieves a 17.58x speedup. These results highlight the feasibility and effec- tiveness of NAIT in real-world applications.
Table 5: Efficiency Comparison of Different Methods on NVIDIA A800 80GB with Batch Size set to 8. API costs follow the official Ope- nAI pricing (S2. m0.0m/mmlliinn input tokens, $8/mil- lion output tokens for GPT-4.1)) GPU costs are es- timated based on the Google Cloud pricing ($1.15 per GPU hour for NVIDIA A800 80GB).
dataset (52k). As
Method
Externally-Indeeenlla
Time
Cost
Alposinans
10.07h
$176.00
Q20
al 2024
3.52h
$4.05
LESS
Li
9.5hh
$11.33
Selectt
20h
$26.08
NATT
1.32h
$1.52
5.3 INTERPRETABILITY ANALYSIS
Transferability of Neural Activation Feature From the results in Table we observe that the neural activation features corresponding different capabilities exhibit remarkable variation in their transferability. For instance, the neural activation feature extracted based on GSM significantly en- hances the overall average performance of LLMs, and it also yields positive effects BBH and CodeX tasks. To further investigate the relationships between the transferability of neural activa- tion features, we introduce Transferability to evaluate each capability during the transfer process. Transferability of capability reflects to what extent i can enhance other capabilities when activated. is defined
Transferability, Acc(i, Acc(j.j)
where (cc j) is the performance on task j activated by feature i, and Acc(j.)) j) is task j' baseline performance with its own activation feature.
Ter/braal
Figure 5: Transferability of neural activation features across capabilities. Each column repre- sents the capability to which the neural activation feature applied. Icons with an outline border indicate the capability's 's performance on its own task, which serves as the baseline reference.
As shown Figure the transferability across capabilities can directly observed. On the one hand, activation features of single capabilities such as GSM and CodeX exhibit evident positive transfer cross-task settings, indicating that logical reasoning and programmatic features possess strong general power. In contrast, the activation features of TydiQA show weaker or even neg- ative transfer, and their performance within the task itself is relatively limited, reflecting both a dependence language-specifer patterns and shorteomings in cross-domain adaptability. On the other hand, when multiple capabilities are aggregated into comprehensive activation direction, the model achieves optimal overall transferability. This finding demonstrates the existence of universal core feature whose neural activation patterns remain stable across tasks, thereby providing solid foundation for the model's 's integrated capabilities.
Direction of Activation Features To further investigate the interpretability of NAIT, we apply
T-SNE to project the neuron activation features into representation space (see Figure 6). The fig- shows that the feature's directions corresponding to different targeted capabilities form rela- tively separable clusters in the representation space, indicating their consistency with the task- specific reasoning mechanisms or knowledge domains. Notably, the comprehensive,
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Published as a conference paper at ICLR 2026
gregated representation across multiple tasks, reveals the general capabilities. This in- dicates that there exist general data or features capable of simultaneously activating multiple distinct abilities, thereby supporting cross-tast knowledge transfer and capability integration.
Selected IT Data Distribution To further analyze the distribution of IT subsets selected by different activation features, performed both qualitative and quantitative analyses (see Appendix D Figure / shows the distribu- tion of the 10% IT dataset selected by NAIT. The results reveal that 58.87% of the subsets overlap across differ- ent capabilities, indicating that NAIT consistently identi- fies stable set of general core data applicable across tasks. However, GSM8K exhibits the highest demand for task-specific data, with 1,034 unique samples, while other tasks like MMLU and CodeX rely more heavily shared core data. This demonstrates NAIT'S ability to balance general-purpose data selection with the re- tention of task-specific samples, ensuring robust perfor- mance across diverse benchmarks. Qualitatively, unlike embedding based methods that focus on surface-level similarity, NAIT prioritizes samples requiring complex task-following and logical reasoning. While methods capture the fundamental formats asso- ciated with factual knowledge, they consistently select the corresponding factual multiple-choice questions.
GSM
Comprehensive
tydiqa
CodeX
Figure 6: Comparison of the direc- tion of activation feature extracted by NAIT across different capabilities.
20.9 191)
1589
1/004
Camprifani
PML
Figure 7: Distribution of 10% IT dataset selected by NAIT The upper bar chart quantifies the intersection sizes among different data subsets, while the connected dots below identify the spe- eific capability combinations corresponding to each intersection.
6 CONCLUSION
In this paper, we propose NAIT, an efficient framework for selecting high-quality IT data by lever- aging neural activation patterns. NAIT identifies the optimal data subset by evaluating the alignment between candidate samples and neural activation features associated with target capabilities. Exper- imental results demonstrate that models fine-tuned on selected data consistently outperform baseline methods. Ablation studies further verify that only a small number of samples are sufficient to accu- rately extract activation features that capture model capabilities. In addition, NAIT exhibits strong robustness and reveals significant cross-task transferability of neural activation features. Further analysis shows that data with logical reasoning and programmatic characteristics tend to activate stronger general capabilities in the model, and that a stable core subset of data exists that can uni- versally enhance performance across variety of tasks.
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ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics Our research focuses on improving the efficiency and interpretability of IT for LLMs through neurally informed data selection. We do not introduce new data collection involving human participants, nor do we use private or personally identifiable data. All datasets employed (e.g.. Alpaca-GPT4, GSM8K, MMLU, BBH, TyDiQA, CodeX) are publicly available and widely used in the research community. We carefully respect data licenses and ensure compliance with privacy and intellectual property standards. Our method aims re- duce the computational and environmental cost of developing LLMSs by selecting smaller subsets of high-quality data, which aligns with the principle minimizing harm. However, as with other LLM research, there is the potential risk of misuse in generating biased harmful outputs. To mitigate this, we explicitly analyze fairness and generalization across different task domains and report trans- ferability results that highlight both strengths and limits of our approach. We disclose all relevant details transparently to foster responsible use of our method.
REPRODUCIBILITY STATEMENT
We have taken several steps to ensure reproducibility of our results. Firstly, we describe the con-
struction process of our neuron activation feature framework in $ with explicit mathematical def- initions. Additionally, detailed descriptions of the model architectures, datasets, hyperparameters, and training setups are provided in $14. Appendix [D] Further experimental details, including ablation studies and comparisons with baselines, are presented in $ a5] $ Appendices G to H All in-domain datasets and IT datasets used in our experiments (e.g., Alpaca-GPT4, Evo- Instruct, Orca- GPT4) are publicly available, and dataset statistics summarized in $ Appendix D To facilitate reproducibility, we commit to open-sourcing the implementation of NAIT, the cross-task neuron feature library, and the Alpaca-NAIT dataset upon acceptance. These resources, along with clear documentation, will allow researchers to fully reproduce and extend our experiments.
ACKNOWLEDGMENTS
This work was supported by National Key Research and Development Program of China (2024YFF0908200), National Natural Science Foundation of China (Grant No. 62376262), the Nat- ural Science Foundation of Guangdong Province of China (2024A 1824A1515000 1012411110001100 66, 2025B15 1515000003))) This work was also supported in part by the Science and Technology Development Fund of Macau SAR (Grant Nos. TBCT/0007/202//KK3 EF2024-00185-SSTT the UM and UMDF (Grant Nos. CBGG-GGG224--0006---ST--UMM
the Tencent AI Lab Rhino- Bird Research Program (Grant No. GF2003-001511FFST the Stanley Ho Medical Development Foundation (Grant No. (HMMDF-A/202/0001), and the National Natural Science Foundation of
MYRG-GRG2025-0236-F
China (Grant No. 62266013).
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Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer. Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettle- moyer, and Omer Levy. LIMA: less is more for alignment. In Alice Oh, Tristan Nau- mann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (eds.), Advances in Neural Information Processing Systems 36: Annual Conference Neural Informa- tion Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 16, 2023, 2023. URL http: http::/papers...ep////// nips. cc/paper wwwes/illaaae htt.le//ppppee222///eesee ac662d74 www.e//488944447..om wTw.4444A0C1111147711141 http:///aaaaaaaattttto-oooooooee
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A LIMITATION
Model Scale Due computational limitations, our analysis is restricted to models with fewer than 20B parameters, Future studies could evaluate this method on larger models to provide insights into its scalability and broader implications for optimizing LLMs.
B THE USE OF LARGE LANGUAGE MODELS (LLMs)
Polishing the Writing with a Large Language Model In preparing this paper, we used large language model refine the writing. Typical applications included: (1) enhancing grammar and style; (2) ensuring consistency of terminology; and (3) improving the quality of translations.
C COMPARATIVE ANALYSIS BEFORE AND AFTER IT
Table 6: Comparison of model functions before and after instruction tuning.
Function
Before Instruction Tuning
After Instruction Tuning
Enhancing instructionfolllwinng ability
Limited completion
Capable of following instructions
Activating lstent abilities
Relies on knowledge from pertaining
Activates abilities not evideat in pretrain- ing. such logical reasoning task de- composition
Enhancing downstream task performance
General knowledge capahilities
Improved downstream task capabilities
The following table 0 compares the main functional differences of models before and after IT. As shown, IT significantly improves soning and task decomposition, and leads to notable gains in downstream task performance.
abilities, activates latent skills such as rea-
Benchmark Settings We evaluated approach various downstream tasks using their data as in-domain data (detailed dataset descriptions are in Appendix D):
Factual Knowledge: We evaluate Massive Multitask Language Understanding (MMLU) dataset (Hendrycks et al. 2021). splitting it into in-domain and test sets, and report 5-shot sults. To assess the generalization capability our model, we additionally conduct experiments on MMLU-Pro (Wang al. 2024c) as OOD benchmark.
Math Capability: We use the Grade School Math (GSM) dataset (Cobbe et al. 2021). divided into in-domain and test sets, and report 5-shot results under the CoT setting. To assess the gen- eralization capability of our model, we additionally conduct experiments on SVAMP (Patel et al. 2021 as OOD benchmark.
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We assess the Big-Bennc--Haid (BBH) dataset (Suzgun al. 2023 with in-domain and test splits, and report 5-shot results under the CoT setting.
Reasoning:
lingual QA dataset, using in-domain and test splits, and report 5-shot results with the multi- setup. To assess the generalization capability of our model, additionally conduct experiments on XQuAD (Artetxe et al. 2019) as an OOD benchmark.
Multilingual Understanding: We evaluate the TyDiQA benchmark (Clark et al.
2020
Coding Capability: We activate the coding ability using BigCodeBench (Zhuo et al. 2024 and evaluate in HumanEval (Chen et al.) 2021). reporting pass@ 10 results with a sampling temper- ature of 0. 8. To assess the generalization capability of our model, additionally conduct experiments on MBPP (Austin et al., (021) as an OOD benchmark.
D DETAILS OF TRAINING AND EVALUATION SETUP
Statisties of the in-domain, IT, and Test dataset Table summarizes the statisties of the In- domain, IT, and Test datasets.
Table 7: Statistics of the Number of in-domain, IT, and Test datasets Used in Experiment.
Targeted Ability-
Faciual Knowledge
Mathenstical
Reusoning
Ability
Muhiilinuual Understindnng
Reasoning
DownStreem Tisk
MMLU
PMLLU-Pr
GSM
SVAMP
HumanEval
BigCodeBench
MBPP
TydiQ4
XQ-AD
BRH
In-domain Dataset
1,531
7,473
1,140
49,400
4,556
Alpaca-GPT4 Dataset
$2.002
Erol-Instruct IT Dataset
70000
Oral-GPT4 IT Dataset
1000000
Dataset
14042
3800
1319
164
900
1,200
1080
Test few-Shot
5
Baselines Settings Our comparison baselines cover a variety of advanced methods for evaluating and improving IT datasets:
Alpaca-GPT4 (Peng et al. 2023): A widely utilized IT dataset that leverages a self-instruct
methodology, enabling the autonomous generation of instructions through the advanced capa- bilities of GPT-4.
LIMA (Zhou et 2023): It primarily comprises 1,000 meticulously crafted, high-quality IT data points designed to enhance the aligmment capabilities of LLMs.
AlpaGasus (Chen et al. 2024): This approach employs the advanced capabilities of ChatGPT to evaluate and selectively curate data from the original Alpaca dataset.
Q22 (Li al. 2024): : It functions by training precursor model and assessing the quality of the instructional data based on two distinct loss values derived from this model.
Select[T (Liu al. 2024b): novel method that leverages the intrinsic uncertainty of LLMs to select high-quality IT data without requiring external resources.
Fine-tuning Parameters The model is fine-tuned over 3 epochs with batch size 228 to ensure efficient learning while avoiding overfitting. The optimization process employs the Adam optimizer with hyperparameters set to 31 = 0. 9 and 32 = O. 999,, which are standard values for stable and effective training. The learning rate follows a cosine decay schedule, starting 2e-5 and decreasing gradually to 0, strategy known to improve convergence by reducing oscillations during later training stages. To maximize the model's performance, we utilize an input sequence length of 2.48 8 tokens, as this configuration has been demonstrated to be effective for LLMs in handling long-context tasks. T provides an overview of the key hyperparameters used during training on A800 GPUs with node. For the main experiments, of 128. For ablation studies, we reduce the training to 1 epoch and adjust the batch size (32 for Llama-2-14B, 64 for Llama-3-8B) to accommodate different model sizes.
train for 3 epochs with batch size
Text Generation Settings For text generation, we employ greedy search for computational ef- ficiency, and set the temperature 0.8 and the top-p to 0. 95 following the HumanEyal set-
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Table 8: Hyperparameter settings for supervised fine-tuning.
Value
Parameter Key
Main Experiment
A Ablation Experiment
Learning rate Cutoff length
2.0e-5
2048
LR scheduler
cosine
bf16
True
Warmup ratio
0.03
Weight decay
0.1
Epoch
3
Batch size
128
32 (14B) 64 (8B)
Table 9: Performance comparison of baselines of activating targeted ability using 10% of the IT data. (e.g.. +MMLU refers to the process where an in-domain dataset to guide the IT data selection.) The Bold represents the best performance respectively in each column.
Factual Knowledge
Multilingual Understanding TydiQA XQuAD
General Reasoning BBH
Method
Test
MMLU MMLU-Pro
AVG
+MMLU
EMBEDDING
46.29
21.07
42.97
44.73
36.67
38.35
REPRESENTATION-BASE
45.57
23.0
46.78
46.85
38.15
40.07
LESS
48.12
24.50
43.42
45.51
38.15
39.94
23.61
38.52
41.31
+TydIQA
EMBEDDING-
47.49
22.54
42.4
46.53
38.43
39.48
REPRESENTATION BBASED
45.91
21.04
40.02
46.0
36.02
37.80
LESS
44.68
22.21
45.24
47.67
37.69
39.50
NAIT
46.17
22.82
47.78
49.23
40.00
41.20
+BBH
EMBEDDING
47.51
22.86
43.25
46.66
66.76
39.41
REPRESENTATION
36.78
37.59
LESS
46.85
23.75
46.56
47.67
39.72
39.61
NAIT
47.78
23.36
45.93
48.46
40.46
41.20
ting (Chen et al. 2021), to enhance the creativity and diversity of the generated content while maintaining its accuracy and contextual relevance.
E NAIT VS. OTHER BASELINE WITH ACTIVATING TARGETED ABILITY
In this section, we elaborate on the experiment setting and analysis between NAIT and other Baseline with activating targeted ability, incuding embedding- methods, representation method (Zhang et al. 2018 Hanawa et al. 202T) the gradient-based method (LESS (Xie et al. 2023)
Experiment Setting
Embedding-baseed Method: We utilize the base model (Llama-2-766 2- to extract the static in- put embeddings. Specifically, we compute the mean the embeddings across all tokens in the sequence obtain a global vector representation for each data sample.
Representation-basse Method (Zhang et al. 2018 Hanawa al. 2021): We extract the hidden states of all tokens from the final decoder layer and apply mean pooling to derive a deep semantic representation of the input.
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LESS (Xie et al. estimating their influence on the target task via low-rank gradient embeddings. We utilize conver- sation format of Alpaca-GPT4 as the candidate pool. Following the standard of our experiment, we first perform a epoch LoRA warmup to obtain feature checkpoints. Then, using BBH, Ty- diQA, and MMLU provided by LESS target validation sets for gradient estimation, compute influence scores and select the top-k samples from Alpaca-GPP that are expected to minimize the validation loss on these targets.
2023): As representative gradient-based approach, LESS selects samples by
Result Analysis While LESS achieves high scores on targeted tasks (e.g., 48.12 on MMLU vs. NAIT'S 47,81), ) suffers from overfitting to the target validation set. This results poor general- ization, evidenced by its lower average score across all tasks (39.94) compared to NAIT (41.31). NAIT maintains robust performance across both targeted and non-targeted settings (c.g.. TydiQA and BBH), demonstrating superior transferabilityy Embedding- -based and representation methods rely on surface-level semantic similarity. While computationally cheap, they often fail to capture complex task-specific capabilities. Gradient-based methods (e.g.. LESS) calculate influence func- tions involving gradients and Hessian approximations. This approach is computationally prohibitive and memory-intensee for large-scale models, despite its precision specific targets.
F ALGORITHMIC FRAMEWORK
In this section, present the detailed pseudo-code for proposed method, discussed in Sec- tion 3.1 Algorithm outlines the complete procedure, including the extraction of neuron activation features and the subsequent activation seature-guiedd data selection.
G THE DETAILS OF DIFFERENT SELECTION STRATEGIES
Table 10 compares selection strategies using 10% of the IT dataset. NAIT achieves the best per- formance with a +3.000 +.0000% improvement over the baseline (Alpaca-GPTa) GPT4), while other strategies like Hard (PPL) and Easy (Length) significantly reduce performance. This highlights NAIT'S effective- ness and the importance of careful data selection.
Table 10: Performance comparison of different selection strategies using 10% of the IT data. The Bold represents the best performance respectively each column. (t) indicates the perfor- mance improvement relative to the ID 01 baseline.
Overall
Method
MMLU
BBH
GSM
TydiQA
CodeX
AVG
A A(t)
Alpaca-GPT4
46.87
39.94
14.63
39.48
27.87
34.16
Hard (PPL)
45.52
22.22
8.72
27.87
20.56
24.98
-6.877%
Easy (PPL)
46.78
38.15
15.39
33.12
28.93
32.47
-5.53%
Hard (Length)
46.10
36.94
16.00
34.32
30.89
32.85
-3.83%
+ Easy (Length)
44.15
10,28
6.37
28.07
17.00
21.17
-38.03%
+ NAIT
46.83
40.02
16.53
46.09
26.44
35.18
+3.00%
H NAIT FOR CROSS-LINGUAL
we further evaluate the cross-lingual data selection capability of NAIT on the multilingual IT dataset in the context of machine translation. We adopt the powerful ALMA-7b (Xu al. 2024b) model as the backbone and conduct experiments on four representative translation directions: English-German German-Fngliss mental results demonstrate that the NAIT method outperforms both random selection (Rand.) and using the full dataset (Full) most evaluation metrics. Notably, NAIT achieves the highest overall
As shown in Table 11
Chinese-Fnglisse and English-Chinese. The experi-
GPT4
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Algorithm Neuron-aware Instruction-tuning Data Selection
Input: In-domain data P, Instruction data Pins Model M, Activation layers L, Selection budget
top-k
Output: Selected subset D selected
1: Stage 1: Targeted Ability Activation Capture
2: Initialize activation difference set AA +- for each layer E L
3: for each in-domain sample P. = (41, 12,12,.... (.,,kx).. E do
4:
for each layer 1 L do
5: Extract activations (t,) and (0 from M
AA + A (tx) (t)) 7: AA + AAT (
6:
> Compute activation change from first to last token
8: end for
9: end for
10: Stage 2: Direction Extraction via PCA
11: Initialize direction vectors V +
12: for each layer 1 = 1 ... do
13:
+ PCA (C)(AA(1)
D. Get First Principal Component
Jdiff PP| Σ PP| (AK) (4))
15: if Maitt Vi < 0 then
> Align Direction With Activation Feature
Vi -v
17: end if
V V {vr)
19: end for
Stage Activation Feature-guided Data Scoring
21: Initialize scores S < 0
22: for each sample y E Dins do Extract Activation A using M(y)
24: $g - E/ (A vt)
> Project Instruct-tuning Data Onto Target Direction
25: S S-SU [sy)
26: end for
27:
Stage 4: Data Ranking & Selection
28:
Dselectod + top-k(S)
return Deelected
Table 11: Overall results on machine translation with LLMs using the ALMA-7b. NAIT indi-
cates selecting the top 30% of data by NAIT, Rand. refers to randomly selecting 30% of data, and Full uses the full dataset.
Dn
De=FEn
Zh==En
En=
Overall
Method
COMET
BLEU
COMET
BLEU
COMET
BLEU
COMET
BLEU
COMET
BLEU
Full
84.54
28.24
83.13
29.06
79.80
22.89
84.15
34.58
82.91
28.69
Rand.
82.44
28.29
81.12
27.20
78.53
21.11
82.12
33.91
81.05
27.63
NAIT
85.14
29.02
82.50
28.70
80.22
23.11
83.89
35.92
82.94
29.19
COMET (82.94) and BLEU (29.19) scores, further highlighting its superior generalization ability and robustness in both target domains and cross cross-lingual scenarios.
I CASE STUDY
To intuitively demonstrate the effectiveness of our method, we provide qualitative comparison of the training samples selected by Embedding, LESS, and NAIT for different downstream benchmarks in Table As observed in the table. baseline methods often select data based surface-level textual similarity, which may not align with the complexity the target task. For instance, in TydiQA, the embedding method selects translation tasks but may not align with the required question answering.
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Table 12: Qualitative comparison of examples selected by Embedding, LESS, and NAIT strategies across different benchmarks. The samples here from NAIT are outside of the intersection of general core data.
Targeted ability activated by TydiQA
Selected by Embedding
Selected by LESS
Selected NAIT
Instruction: Trunslate the phrase "Yechh" into Hindi.
Instruction: Construct mathematical equation for the following problem:
Instruction:
Create two authentic Thai dishes.
At party, there are 3 types of food. Each type of food is served three times euch. Guests chanse only of each food serving. How many different combinations are there for the guests?
Output: Great! Here are the two Thai dishes!
The phruse "Yechh" un expression of disgust dislike and is a word with direct translation into Hindi, However, similar expression of disgust Hindi would he arerrarre" or "qğTr" (Chhoc) or "ransgan"" (chidam) or "aरर""" कहडंरा zip" (arey baap re).
Oulput:
1. Thai Green Curry Chicken
Ingredients:
lbs boncless, skinless chicken
Output: number of combinations 3 x 3 x 27
breasts
1 can (13/14-ounce) coconut milk
1/4 cup green curry paste [..]
Targeted ubility activated by BBH
Selected by Embedding
Selected by LESS
Selected NAIT
Instruction: Given a document, find the most frequertly used words.
Instruction: Convert the following docu- ment into a list of five bullet points.
Instruction: Design an algorithm to solve the Hight Queens puzzle
Input: John went the store to buy candy.
Input: Sports play key role in the lives of many people. It can tench young people about leadership and teamwork, well important values like perseverance and ca- maraderie. Sporting events bring commuu- nities together and also provide an oppor- tunity for people to come and support their favourite teams players.
Oulput: The Eight Queens puzzle is classic peoblem where the gool is to place cight queens an 8x8 chessboard in such a way that no two queens threaten each other. This means that no two queens can he placed in the same row, column, or di- agonal
Output: Based the given sentence, the most frequently used words are:
"to" which uppears times the sen- tence.
"the" which appears time in the sen-
lence.
One solution to this problem is to back- tracking. Here is an algorithm to solve the Eight Queens puzzle using backtracking: 1. Start with an empty 8x8 chessboard and add first queen to first column.
- "John" - which appears I time in the sen-
Output: Sports play n key role in the lives many people
tence.
"wen"" - which uppears I time in the sea-
Sports can teach young people about leadership and teamwork, as well portant values like perseverance and cama- ruderie.
lence.
row in the
"store" - which appears time in the sen-
lence.
2. Move to the next row and try placing a queen in each column. Check if the posi- tion is safe by verifying if any of the pre- viously placed queens attacking the cur- rent pusition. it'sss safe position, place a queen in that column.
"buy".. which appears time the
Sporting events bring communities to- gether.
tonce.
"some" which appears time sen-
Sperting events provide an opportu- nity fur people to come and suppori their favourite teams players.
lence.
"candy" which appears time the sentence. Selected by Embbeddig
Sports promote important values and bring people together.
3. 3.Boolean function is safe(board, row, col): [..]]
Targeted ability activated by MMLU
Selected by LESS
Selected by NAIT
Instruction: Rank the following four choices in order from least likely to most likely.
Instruction: Choose the best answer from the given list of choices.
Instruction: Generate an example a
multiple choice question related the fol- lowing sentence.
Inpul: Question: Which the following
Input: Taking long walk B. Eating two slices of chocolate cake
benefit of cloud computing?
Output: Entemology is the science which deals with:
A. Lower cost
Writing persunsive essay
Reliability
human behavior
D. Drinking glass of orange juice Output: It's not possible to rank the given chosces in order from least likely to most likely without additionul context or infor- mation about the person's habits, prefer- ences or circumstances. Could you pro- vide more information so better you?
C. Incrensed scalahility
B) the study of insects
D. All of the above Oufput: D. All of the above.
CJ marine life
D) the solar system
Correct answer: B
For the general reasoning ability, the Embedding and LESS strategies select simple tasks such as word frequency counting or text summarization. In contrast, NAIT identifies an algorithmic problem that requires multi-step logical reasoning and coding synthesis. Similarly, for MMLU, NAIT selects a knowledge-style question similar to other methods that aligns well with the knowledge-intensive of the benchmark.
21


Reviews:
[{"review_id": "4XrmSpIItm", "reviewer": "Reviewer_2hWE", "summary": "- This paper introduces NAIT, a novel framework for selecting high-quality data for the instruction tuning LLMs. \n- The proposed NAIT method first captures a characteristic activation feature for a target capability by processing a small set of examples and applying PCA to their neuron activations. Second, it scores and selects data from a larger candidate pool by measuring the alignment between the candidate data's neuron activations and this characteristic feature.", "questions": "Same as the weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- While neuron activation analysis has been used to interpret LLMs, its application as a direct signal for instruction data selection is novel and interpretable. \n- The paper is well written, and the experiments are conducted on a wide range of datasets and base models.\n- The cost-efficiency analysis is a crucial and compelling strength, showing improvements in speed and cost, which is a highly practical advantage in the LLM era.\n- The interpretable analysis on transferability and the relation of different activation features supports the motivation of the proposed method.", "weaknesses": "- The framework's first step requires a capabilities-specific in-domain dataset. For the tasks evaluated (MMLU, GSM, etc.), these datasets are readily available from established benchmarks. However, if one wishes to enhance a more abstract or novel capability, curating a high-quality, representative in-domain dataset becomes a significant, and potentially subjective, bottleneck. \n- This paper should provide a few qualitative examples to illustrate the difference between data points in the \"core set\" (selected for most capabilities) and those that are task-specific (e.g., selected only when targeting CodeX)? \n- The related work section correctly identifies Loss and Gradient-based Coreset Sampling Methods as a relevant category of data selection techniques. However, no gradient-based method was included as a baseline in the experimental comparisons.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AojKgm5k1N", "reviewer": "Reviewer_wy8Y", "summary": "This paper introduces a neuron-aware framework for selecting instruction-tuning data based on neuron activation similarity. Instead of relying on external models or uncertainty metrics, NAIT extracts neuron activation features from small in-domain reference sets and scores candidate samples by alignment with these features. Experiments  show that training on 10% of the selected Alpaca-GPT-4 data improves average performance across multiple benchmarks and domains.", "questions": "Did you compute ΔA(Dn) analogously to Eq. (3), then apply the same PCA transform learned on in‑domain features before scoring? Do you use cosine similarity or dot product? Any per‑sample length or norm? Providing an algorithm box would be better.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Strong data‑efficiency. NAIT with 10% of Alpaca‑GPT‑4 matches/exceeds full‑data tuning and outperforms several selection baselines.\n- The neuron‑feature construction and per‑candidate alignment score are conceptually simple and effective across many tasks.", "weaknesses": "- The experiments didn’t compare against some strong recent data selection methods like LESS (ICML 2024).\n- The paper seems doesn’t specify which layers’ activations are used to compute features or why. Since different layers capture different types of information, this choice may matters. explanation or ablations could be added.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8Yzup9IkZI", "reviewer": "Reviewer_SZW9", "summary": "This paper introduces NAIT, a novel framework for efficient instruction tuning data selection by analyzing neuron activation patterns in Large Language Models to identify data that effectively enhances specific or general capabilities. The core hypothesis is that IT data samples are more effective if their neuronal activation patterns closely align with the activation characteristics associated with a target capability. NAIT extracts reusable neuron activation features from in-domain datasets and selects optimal IT samples based on their similarity to these target capability activation features, demonstrating superior performance and cost efficiency compared to existing methods. Experiments show NAIT consistently improves LLM performance (e.g., 3% with only 10% of data), reveals the strong general transferability of logical reasoning and programmatic features, and identifies a stable core subset of data universally beneficial across diverse tasks. Ablation studies reveal that optimal performance is achieved with approximately 30% of the IT dataset selected by NAIT, with larger proportions leading to degradation due to redundant data.  The paper open-sources a cross-task neuron feature library and the Alpaca-NAIT dataset, a high-quality IT dataset curated using the proposed NAIT framework.", "questions": "1. Could you clarify the construction of the “activation feature” and alignment score? Specifically: (a) Do you use the entire model’s neuron activations for the input (all layers) or only a particular layer’s output (e.g., last hidden state)? (b) What is ∆A($P_i$) exactly (activation difference relative to what baseline)? (c) Once you have PCA-compressed features for in-domain and candidate data, is the similarity score simply a dot product or cosine similarity in that feature space? Providing these details (perhaps I missed them in the text).\n2. In the experiment combining all capability features, how was this combination done? Did you union the top-k selections from each capability run, or aggregate the activation features into one composite feature vector for scoring all data at once? The result that this combined approach didn’t outperform the best single-capability selection is interesting – could you elaborate on why you think that happened?\n3. Have you tried applying NAIT with a larger base model or on a larger pool of candidate data? The cost analysis suggests NAIT is efficient on 52k data with a 7B-ish model. If we consider, say, a 70B model and a few hundred thousand instructions, do any new challenges arise (e.g., needing to handle many more neurons, longer activation vectors, etc.)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of using the model’s own internal neuron activation patterns to evaluate data quality is novel and interesting. Unlike prior data selection methods that rely on external scorers or surface heuristics, NAIT directly taps into the model’s internal representation. This provides a more interpretable and fine-grained signal and is externally independent.\n2. NAIT effectively addresses the scenario of enhancing specific capabilities in an LLM. The method is flexible – by providing an in-domain sample set for a desired skill, the framework can pick out relevant training data to boost that skill. Experiments confirm that NAIT tuning on a skill-focused subset yields better performance on that domain’s benchmark than generic fine-tuning.\n3. The approach shows that a 10% subset selected by NAIT can outperform or equal the full 100% data training in many cases. These results reinforce the claim that more data isn’t always better, and smart selection can yield efficiency gains. It’s impressive that NAIT even beat methods like AlpaGasus and Q2Q which leverage GPT4 or uncertainty measures.\n4. A notable strength is the paper’s analysis of neuron activation transferability. Those findings provide insight into what kinds of instructional data are broadly useful. Such analysis adds depth to the work, beyond just performance numbers.\n5. By avoiding external model calls and using efficient feature extraction, NAIT is relatively lightweight. The authors compare computational cost and show significant speedup and cost reduction versus prior methods.", "weaknesses": "1. A practical concern is that NAIT requires a representative in-domain dataset for each target capability as a starting point. In real scenarios, such labeled data may not be readily available for every “capability” one wishes to improve. This reliance potentially limits NAIT’s applicability to cases where one can clearly define and obtain data for the target skill. If the in-domain examples are too few or not truly representative, the quality of the activation feature (and thus the selection) might suffer. The paper does not deeply explore how sensitive the method is to the choice or size of this in-domain set.\n2. One of the claims is that NAIT can enhance specific or general capabilities. However, the results suggest that focusing on a single strong capability (like GSM for math) yielded the highest average boost, even more than combining all capability features. The model fine-tuned on the GSM-selected subset outperformed the model using a multi-capability combined subset (35.42 vs 35.18 AVG). This is somewhat counter-intuitive – ideally, integrating multiple skills would give broader improvement. The fact that the “all capabilities” selection did not significantly outperform single-capability selections raises concerns. It suggests NAIT might struggle to simultaneously optimize for many skills, or that the method for merging activation features may be suboptimal. This limitation should be better addressed: if one wants a generally strong model, do they have to run NAIT for every capability and merge data ad-hoc? \n3. The paper lacks direct comparison to an embedding-based selection baseline. How does NAIT compare to a simpler strategy like using embedding similarity for data selection? For instance, one could embed each candidate instruction (using the same LLM) and each in-domain example, then select those with highest similarity to the in-domain set (or to a mean in-domain embedding). This makes it harder to assess how much benefit comes from the neuron-level analysis versus just using the model’s last-layer representation or other heuristics.\n4. There are some open questions about NAIT’s scalability and certain design choices. The paper would benefit from clarity on which layers or neurons are used for the activation pattern – is it the entire hidden state of the model, a subset of layers, or aggregated over the whole sequence? Additionally, while the authors claim adaptability to different model sizes, it’s not explicitly shown how NAIT scales to very large models (e.g., 70B parameters) – the experiments seem limited to one model family (likely around 7B-13B scale).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1nXF9euh1y", "reviewer": "Reviewer_76SK", "summary": "The paper proposes NAIT, a novel and efficient framework for selecting high-quality instruction tuning (IT) data based on the similarity of neuron activation patterns between candidate data and a target capability's in-domain reference set.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Clarity and Presentation: The paper is well-written and easy to follow.\n\n2. Relevance of Core Idea: The paper successfully addresses the critical insight that the quality of instruction data is more important than the quantity. The proposed method offers a potential, scalable approach for constructing high-quality instruction datasets.", "weaknesses": "1. The central hypothesis, that the effectiveness of instruction data lies in its ability to activate task-relevant neurons, is merely an assumption and is not widely recognized or proven.\n\n2. The use of a small, in-domain reference dataset to extract the neuron activation features (for the target capability) may introduce significant bias into the feature extraction process, potentially limiting the generality of the resulting selection criterion.\n\n3. The primary base model used for experimentation is LLaMA and Mistral. Compared to other similarly-sized open-source LLMs (Qwen2.5, Qwen3) available today, LLaMA and Mistral series are generally considered weaker, which lacks sufficient persuasiveness for the experimental results.\n\n4. The experimental evaluation section uses a limited number of benchmarks and fails to include some newer and more comprehensive evaluation benchmarks (MMLU-pro, GPQA, LiveCodeBench, MBPP, HumanEval, MATH-500, Minerva-Math, OlympaidBench, etc.). This limits the ability to fully assess the efficacy and generalizability of the proposed NAIT method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)