@inproceedings{huang-etal-2025-neuron,
title = "Neuron-Level Differentiation of Memorization and Generalization in Large Language Models",
author = "Huang, Ko-Wei and
Fu, Yi-Fu and
Tsai, Ching-Yu and
Tu, Yu-Chieh and
Cheng, Tzu-ling and
Lin, Cheng-Yu and
Yang, Yi-Ting and
Liu, Heng-Yi and
Liao, Keng-Te and
Juan, Da-Cheng and
Lin, Shou-De",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.812/",
pages = "16077--16091",
ISBN = "979-8-89176-332-6",
abstract = "We investigate how Large Language Models (LLMs) distinguish between memorization and generalization at the neuron level. Through carefully designed tasks, we identify distinct neuron subsets responsible for each behavior. Experiments on both a GPT-2 model trained from scratch and a pretrained LLaMA-3.2 model fine-tuned with LoRA show consistent neuron-level specialization. We further demonstrate that inference-time interventions on these neurons can steer the model{'}s behavior toward memorization or generalization. To assess robustness, we evaluate intra-task and inter-task consistency, confirming that these neuron-behavior associations reflect generalizable patterns rather than dataset-specific artifacts. Our findings reveal modular structure in LLMs and enable controlling memorization and generalization behaviors at inference time."
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<abstract>We investigate how Large Language Models (LLMs) distinguish between memorization and generalization at the neuron level. Through carefully designed tasks, we identify distinct neuron subsets responsible for each behavior. Experiments on both a GPT-2 model trained from scratch and a pretrained LLaMA-3.2 model fine-tuned with LoRA show consistent neuron-level specialization. We further demonstrate that inference-time interventions on these neurons can steer the model’s behavior toward memorization or generalization. To assess robustness, we evaluate intra-task and inter-task consistency, confirming that these neuron-behavior associations reflect generalizable patterns rather than dataset-specific artifacts. Our findings reveal modular structure in LLMs and enable controlling memorization and generalization behaviors at inference time.</abstract>
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%0 Conference Proceedings
%T Neuron-Level Differentiation of Memorization and Generalization in Large Language Models
%A Huang, Ko-Wei
%A Fu, Yi-Fu
%A Tsai, Ching-Yu
%A Tu, Yu-Chieh
%A Cheng, Tzu-ling
%A Lin, Cheng-Yu
%A Yang, Yi-Ting
%A Liu, Heng-Yi
%A Liao, Keng-Te
%A Juan, Da-Cheng
%A Lin, Shou-De
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F huang-etal-2025-neuron
%X We investigate how Large Language Models (LLMs) distinguish between memorization and generalization at the neuron level. Through carefully designed tasks, we identify distinct neuron subsets responsible for each behavior. Experiments on both a GPT-2 model trained from scratch and a pretrained LLaMA-3.2 model fine-tuned with LoRA show consistent neuron-level specialization. We further demonstrate that inference-time interventions on these neurons can steer the model’s behavior toward memorization or generalization. To assess robustness, we evaluate intra-task and inter-task consistency, confirming that these neuron-behavior associations reflect generalizable patterns rather than dataset-specific artifacts. Our findings reveal modular structure in LLMs and enable controlling memorization and generalization behaviors at inference time.
%U https://aclanthology.org/2025.emnlp-main.812/
%P 16077-16091
Markdown (Informal)
[Neuron-Level Differentiation of Memorization and Generalization in Large Language Models](https://aclanthology.org/2025.emnlp-main.812/) (Huang et al., EMNLP 2025)
ACL
- Ko-Wei Huang, Yi-Fu Fu, Ching-Yu Tsai, Yu-Chieh Tu, Tzu-ling Cheng, Cheng-Yu Lin, Yi-Ting Yang, Heng-Yi Liu, Keng-Te Liao, Da-Cheng Juan, and Shou-De Lin. 2025. Neuron-Level Differentiation of Memorization and Generalization in Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 16077–16091, Suzhou, China. Association for Computational Linguistics.