DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models

• URL: http://arxiv.org/abs/2604.27929v1
• Date: Thu, 30 Apr 2026 14:31:46 GMT
• Title: DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models
• Abstract summary: Current methods can change personalities but reduce overall performance.<n>We find that Neurons are multifunctional, connecting personality traits and general knowledge.<n>We propose DPN-LE, which identifies personality-specific neurons by contrasting steering activations between high-trait and low-trait samples.
• Score: 25.763216553110386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the widespread adoption of large language models (LLMs), understanding their personality representation mechanisms has become critical. As a novel paradigm in Personality Editing, most existing methods employ neuron-editing to locate and modify LLM neurons, requiring changes to numerous neurons and leading to significant performance degradation. This raises a fundamental question: Are all modified neurons directly related to personality representation? In this work, we investigate and quantify this specificity through assessments of general capability impact and representation-level patterns. We find that: 1) Current methods can change personalities but reduce overall performance. 2) Neurons are multifunctional, connecting personality traits and general knowledge. 3) Opposing personality traits demonstrate distinctly mutually exclusive representation patterns. Motivated by these findings, we propose DPN-LE (Dual Personality Neuron Localization and Editing), which identifies personality-specific neurons by contrasting MLP activations between high-trait and low-trait samples. DPN-LE constructs layer-wise steering vectors and applies dual-criterion filtering based on Cohen's $d$ effect size and activation magnitude to isolate mutually exclusive neuron subsets. Sparse linear intervention on these neurons enables precise personality control at inference time. Using only 1,000 contrastive sample pairs per trait, DPN-LE intervenes on $\sim$0.5\% of neurons while achieving competitive personality control and substantially better capability preservation across reasoning tasks. Experiments on LLaMA-3-8B-Instruct and Qwen2.5-7B-Instruct demonstrate the effectiveness and generalizability of our approach.

Related papers

• Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts [57.98275914877019]
  We show that across a wide range of open-weight Transformers, a subset of neurons remains consistently highly activated during inference across tasks of multiple capability dimensions.<n>By probing along the cross-task activation strength, an extremely sparse subset is isolated, whose removal causes a collapse in model behavior.<n>Our analysis reveals that keystone neurons are a stable and intrinsic neuron subset of the model that is largely established during pretraining.
  arXiv  Detail & Related papers  (2026-05-24T03:31:07Z)
• Know Thyself by Knowing Others: Learning Neuron Identity from Population Context [9.798773806523114]
  We present the first systematic scaling analysis for neuron-level representation learning.<n>We show that increasing the number of animals used during pretraining consistently improves downstream performance.<n>Results highlight how large, diverse neural datasets enable models to recover information about neuron identity that generalize across animals.
  arXiv  Detail & Related papers  (2025-12-01T02:28:04Z)
• On Relation-Specific Neurons in Large Language Models [55.38860695310484]
  In large language models, certain emphneurons can store distinct pieces of knowledge learned during pretraining.<n>We study the LLama-2 family on a chosen set of relations, with a textitstatistics-based method.<n>Our experiments demonstrate the existence of relation-specific neurons.
  arXiv  Detail & Related papers  (2025-02-24T17:33:18Z)
• Neuron Empirical Gradient: Discovering and Quantifying Neurons Global Linear Controllability [14.693407823048478]
  We show that the neuron empirical gradient (NEG) captures how changes in activations affect predictions.<n>We also show that NEG effectively captures language skills across diverse prompts through skill neuron probing.<n>Further analysis highlights the key properties of NEG-based skill representation: efficiency, robustness, flexibility, and interdependency.
  arXiv  Detail & Related papers  (2024-12-24T00:01:24Z)
• Neuron-based Personality Trait Induction in Large Language Models [115.08894603023712]
  Large language models (LLMs) have become increasingly proficient at simulating various personality traits. We present a neuron-based approach for personality trait induction in LLMs.
  arXiv  Detail & Related papers  (2024-10-16T07:47:45Z)
• Interpreting the Second-Order Effects of Neurons in CLIP [73.54377859089801]
  We interpret the function of individual neurons in CLIP by automatically describing them using text.<n>We present the "second-order lens", analyzing the effect flowing from a neuron through the later attention heads, directly to the output.<n>Our results indicate that an automated interpretation of neurons can be used for model deception and for introducing new model capabilities.
  arXiv  Detail & Related papers  (2024-06-06T17:59:52Z)
• Neuron to Graph: Interpreting Language Model Neurons at Scale [8.32093320910416]
  This paper introduces a novel automated approach designed to scale interpretability techniques across a vast array of neurons within Large Language Models. We propose Neuron to Graph (N2G), an innovative tool that automatically extracts a neuron's behaviour from the dataset it was trained on and translates it into an interpretable graph.
  arXiv  Detail & Related papers  (2023-05-31T14:44:33Z)
• And/or trade-off in artificial neurons: impact on adversarial robustness [91.3755431537592]
  Presence of sufficient number of OR-like neurons in a network can lead to classification brittleness and increased vulnerability to adversarial attacks. We define AND-like neurons and propose measures to increase their proportion in the network. Experimental results on the MNIST dataset suggest that our approach holds promise as a direction for further exploration.
  arXiv  Detail & Related papers  (2021-02-15T08:19:05Z)
• Compositional Explanations of Neurons [52.71742655312625]
  We describe a procedure for explaining neurons in deep representations by identifying compositional logical concepts. We use this procedure to answer several questions on interpretability in models for vision and natural language processing.
  arXiv  Detail & Related papers  (2020-06-24T20:37:05Z)
• Non-linear Neurons with Human-like Apical Dendrite Activations [81.18416067005538]
  We show that a standard neuron followed by our novel apical dendrite activation (ADA) can learn the XOR logical function with 100% accuracy. We conduct experiments on six benchmark data sets from computer vision, signal processing and natural language processing.
  arXiv  Detail & Related papers  (2020-02-02T21:09:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.