Neuron-Aware Data Selection In Instruction Tuning For Large Language Models

Xin Chen; Junchao Wu; Shu Yang; Runzhe Zhan; Zeyu Wu; Min Yang; Shujian Huang; Lidia S. Chao

cs.CLMar 13, 2026

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,

Organizations: NLP2CT Lab, Department of Computer and Information Science, University of Macau · Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology · National Key Laboratory for Novel Software Technology, Nanjing University · Provable Responsible AI and Data Analytics Lab, KAUST ·

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.

Figures & tables

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 an activation feature through a dimensionality reduction method. Finally, we evaluate the feature alignment score between this activation feature and the model’s activation on each candidate dataset to guide data selection.

Method	Feature Source	Externally-Independent	Gradient-Free	Targeted Ability	Cost-Effective	Interpretability
LIMA ( Zhou et al., 2023 )	Human	✔	✔	✗	✗	✗
Instruction Mining ( Cao et al., 2023 )	PPL & Reward scores & …	✔	✔	✗	✗	$\bigcirc$
AlpaGasus ( Chen et al., 2024 )	ChatGPT Score	✗	✔	✗	✗	✗
SelectIT ( Liu et al., 2024b )	Multi-granularity Uncertainty	✔	✔	✗	✗	$\bigcirc$
LESS ( Xia et al., 2024 )	Gradient-based	✔	✗	✔	✗	$\bigcirc$
Nait (Ours)	Neuron Activation	✔	✔	✔	✔	✔

Table 1: Comparison of our method and existing methods. ✔ indicates the presence of a feature, ✗ indicates its absence, and

\bigcirc

denotes partial support.

System ID $\downarrow$	Method $\downarrow$	Factual Knowledge		Mathematical Reasoning		Coding Ability		Multilingual Understanding		General Reasoning
	Test $\rightarrow$	MMLU	MMLU-Pro	GSM	SVAMP	H-Eval	MBPP	TydiQA	XQuAD	BBH	AVG	$\Delta$ ( $\uparrow$ )
Full Fine-tuning
01	Alpaca-GPT4 ( Peng et al., 2023 )	46.87	21.89	14.63	39.00	27.87	51.58	39.48	42.99	39.94	36.03	-
02	LIMA ( Zhou et al., 2023 )	45.20	23.04	15.76	37.67	27.75	46.56	44.92	44.72	39.91	36.17	+0.39%
03	01 + AlpaGasus ( Chen et al., 2024 )	43.21	21.96	13.34	36.67	23.94	46.08	44.70	46.84	39.91	35.18	-2.34%
04	01 + Q2Q ( Li et al., 2024 )	46.73	21.50	14.50	35.00	25.19	44.97	44.41	48.44	40.34	35.68	-0.98%

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 respectively in each column.

\Delta

(

\uparrow

) indicates the performance improvement relative to the ID 01.

Figure 2: Performance at different proportions of the IT dataset. Task performance in 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.

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.

Intensity	MMLU	BBH	H-Eval	TydiQA	H-Eval	Overall
Intensity	MMLU	BBH	H-Eval	TydiQA	H-Eval	AVG	$\Delta$ ( $\uparrow$ )
Random	47.14	39.21	14.13	44.16	25.55	34.04	-
High	46.83	40.02	16.53	46.09	26.44	35.18	+3.35%
Low	46.30	28.15	10.61	36.12	20.15	28.27	-17.54%

Table 3: Performance comparison across datasets at 10% IT dataset under different neuron activation levels.

Method	MMLU	BBH	GSM	TydiQA	H-Eval	Overall
Method	MMLU	BBH	GSM	TydiQA	H-Eval	AVG	$\Delta$ ( $\uparrow$ )
LLama-2-13b
Alpaca	53.90	45.00	20.85	44.13	34.84	39.74	-
+Random	52.70	47.59	22.06	44.66	36.79	40.76	+2.57%
+ Nait	54.10	47.04	24.11	48.89	38.53	42.53	+7.02%
Mistral-7b

Table 4: Effectiveness of Nait Across Models. Alpaca and Random are trained on the full dataset and a random of 10% subset, while Nait selects a 10% subset.

Figure 4: Comparison of Nait across different IT dataset. Task performance in Nait refers to using the all in-domain dataset to guide IT data selection and evaluate task performance.

Method	Externally-Independent	Time	Cost
AlpaGasus ( Chen et al., 2024 )	✗	19.07h	$178.02
Q2Q ( Li et al., 2024 )	✗	3.52h	$4.05
LESS ( Liu et al., 2024b )	✓	9.86h	$11.33
SelectIT ( Xia et al., 2024 )	✓	23.20h	$26.68
Nait	✓	1.32h	$1.52

Table 5: Efficiency Comparison of Different Methods on NVIDIA A800 80GB with Batch Size set to 8 . API costs follow the official OpenAI pricing (

2.00/million input tokens,

8/million output tokens for GPT-4.1); GPU costs are estimated based on the Google Cloud pricing ($1.15 per GPU hour for NVIDIA A800 80GB).

Figure 5: Transferability of neural activation features across capabilities. Each column represents the capability to which the neural activation feature is applied. Icons with an outline border indicate the capability’s performance on its own task, which serves as the baseline reference.

Figure 6: Comparison of the direction of activation feature extracted by Nait across different capabilities.

Figure 7: Distribution of 10% IT dataset selected by Nait . The upper bar chart quantifies the intersection sizes among different IT data subsets, while the connected dots below identify the specific capability combinations corresponding to each intersection.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Function	Before Instruction Tuning	After Instruction Tuning
Enhancing instruction-following ability	Limited to text completion	Capable of following instructions
Activating latent abilities	Relies on knowledge from pertaining	Activates abilities not evident in pretraining, such as logical reasoning or task decomposition
Enhancing downstream task performance	General knowledge capabilities	Improved downstream task capabilities

Appendix

Table 6: Comparison of model functions before and after instruction tuning.

Targeted Ability $\rightarrow$	Factual Knowledge		Mathematical Reasoning		Coding Ability		Multilingual Understanding			General Reasoning
DownStream Task	MMLU	MMLU-Pro	GSM	SVAMP	HumanEval	BigCodeBench	MBPP	TydiQA	XQuAD	BBH
In-domain Dataset	1,531	-	7,473	-	-	1,140	-	49,400	-	4,556
Alpaca-GPT4 IT Dataset	52,002
Evol-Instruct IT Dataset	70,000
Oral-GPT4 IT Dataset	100,000
Test Dataset	14042	2800	1319	900	164	-	974	900	1,200	1080

Appendix

Table 7: Statistics of the Number of in-domain, IT, and Test datasets Used in Experiment.

Parameter Key	Value
	Main Experiment	Ablation Experiment
Learning rate	2.0e-5
Cutoff length	2048
LR scheduler	cosine
bf16	True
Warmup ratio	0.03

Appendix

Table 8: Hyperparameter settings for supervised fine-tuning.

Method $\downarrow$	Factual Knowledge		Multilingual Understanding		General Reasoning
Test $\rightarrow$	MMLU	MMLU-Pro	TydiQA	XQuAD	BBH	AVG
+MMLU
Embedding	46.29	21.07	42.97	44.73	36.67	38.35
Representation-based	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
Nait	47.81	23.61	47.16	49.47	38.52	41.31

Appendix

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.

Method	MMLU	BBH	GSM	TydiQA	CodeX	Overall
Method	MMLU	BBH	GSM	TydiQA	CodeX	AVG	$\Delta$ ( $\uparrow$ )
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	-26.87%
+ 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%

Appendix

Table 10: Performance comparison of different selection strategies using 10% of the IT data. The Bold represents the best performance respectively in each column.

\Delta

(

\uparrow

) indicates the performance improvement relative to the ID 01 baseline.

Method	En $\Rightarrow$ De		De $\Rightarrow$ En		Zh $\Rightarrow$ En		En $\Rightarrow$ Zh		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

Appendix

Table 11: Overall results on machine translation with LLMs using the ALMA-7b. Nait indicates selecting the top 30% of data by Nait , Rand. refers to randomly selecting 30% of data, and Full uses the full dataset.

Targeted ability activated by TydiQA
Selected by Embedding	Selected by LESS	Selected by NAIT
Instruction: Translate the phrase ”Yechh” into Hindi. Output: The phrase “Yechh” is an expression of disgust or dislike and is not a word with a direct translation into Hindi. However, a similar expression of disgust in Hindi would be “.phahaH” or “chii” (Chhee) or “caDham” (chidam) or “are baapa re” (arey baap re).	Instruction: Construct a mathematical equation for the following problem: At a party, there are 3 types of food. Each type of food is served three times each. Guests can choose only 1 of each food serving. How many different combinations are there for the guests? Output: number of combinations = 3 x 3 x 3 = 27	Instruction: Create two authentic Thai dishes. Output: Great! Here are the two Thai dishes! 1. Thai Green Curry Chicken Ingredients: - 1 1/2 lbs boneless, skinless chicken breasts - 1 can (13/14-ounce) coconut milk - 1/4 cup green curry paste […]
Targeted ability activated by BBH
Selected by Embedding	Selected by LESS	Selected by NAIT
Instruction: Given a document, find the most frequently used words. Input: John went to the store to buy some candy. Output: Based on the given sentence, the most frequently used words are: - ”to” - which appears 2 times in the sentence. - ”the” - which appears 1 time in the sentence. - ”John” - which appears 1 time in the sentence. - ”went” - which appears 1 time in the sentence. - ”store” - which appears 1 time in the sentence. - ”buy” - which appears 1 time in the sentence. - ”some” - which appears 1 time in the sentence. - ”candy” - which appears 1 time in the sentence.	Instruction: Convert the following document into a list of five bullet points. Input: Sports play a key role in the lives of many people. It can teach young people about leadership and teamwork, as well as important values like perseverance and camaraderie. Sporting events bring communities together and also provide an opportunity for people to come and support their favourite teams or players. Output: - Sports play a key role in the lives of many people - Sports can teach young people about leadership and teamwork, as well as important values like perseverance and camaraderie. - Sporting events bring communities together. - Sporting events provide an opportunity for people to come and support their favourite teams or players. - Sports promote important values and bring people together.	Instruction: Design an algorithm to solve the Eight Queens puzzle Output: The Eight Queens puzzle is a classic problem where the goal is to place eight queens on an 8x8 chessboard in such a way that no two queens threaten each other. This means that no two queens can be placed in the same row, column, or diagonal. One solution to this problem is to use backtracking. Here is an algorithm to solve the Eight Queens puzzle using backtracking: 1. Start with an empty 8x8 chessboard and add the first queen to the first row in the first column. 2. Move to the next row and try placing a queen in each column. Check if the position is safe by verifying if any of the previously placed queens is attacking the current position. If it’s a safe position, place a queen in that column. 3.Boolean function is safe(board, row, col): […]
Targeted ability activated by MMLU

Appendix

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.

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Open paper AskPDF arXiv

Instruction fine-tuning is employed to enhance the instruction-following ability of large language models (LLMs). As the amount of instruction fine-tuning data increases, selecting the optimal core set becomes particularly important. However, ensuring the diversity of the core set remains a significant challenge. Existing methods predominantly distinguish different training data based on the text features themselves, decoupled from LLMs' own understanding and representation of the data. To address this issue, we propose a Model-Aware Diverse Core Set Selection method, which distinguishes data features based on the neural activation states during LLM inference. This approach serves as an efficient instantiation of coverage-based selection using model-intrinsic activation features to ensure the diversity in the core set. We extensively evaluate our method on six benchmarks that cover five distinct tasks. In our method, the core set selected by the 3B-parameter LLM performs effectively when utilized to fine-tune larger models with 7B, 8B, and 13B parameters. Experimental results on the Alpaca-GPT4 dataset, which comprises 52K instruction-response pairs, show that the core set, sized at 15% of the original dataset and selected by Llama-3.2-3B-Instruct, achieves an average improvement of 2.5% when fine-tuning four larger base models compared with training on the full dataset. The experimental results demonstrate that our method enhances model performance on multiple downstream tasks while reducing data requirements.

Abstract

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