Your LLM, Your Style: Behavioral Mode Axes for LLM Behavioral Control

• URL: http://arxiv.org/abs/2608.10703v1
• Date: Tue, 11 Aug 2026 09:25:47 GMT
• Title: Your LLM, Your Style: Behavioral Mode Axes for LLM Behavioral Control
• Abstract summary: Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream decision making.<n>Existing LLM personality studies largely rely on self-report questionnaires administered in first-person settings.<n>We introduce a situated behavioral-data framework for studying and controlling LLM behavioral personality.
• Score: 15.236237731175192
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream decision making. Existing LLM personality studies largely rely on self-report questionnaires administered in first-person settings, making the resulting profiles sensitive to surface elicitation choices and poorly grounded in concrete model behavior. In this work, we introduce a situated behavioral-data (B-data) framework for studying and controlling LLM behavioral personality. We construct 3,200 contrastive behavioral scenarios spanning 20 behavioral patterns and four prompt registers, grounded in validated psychometric facets such as BFI-2, DOSPERT, and HEXACO. Using this framework, we find that LLMs exhibit stable and model-specific behavioral profiles, while also revealing register-dependent shifts across first-person decisions, advice-giving, and task execution. We then show that these behavioral patterns can be controlled through Behavioral Mode Axes (BMAs), activation-space directions derived from contrastive behavioral traces. Compared with response-derived BMAs, which are more prone to trait drift, thought-derived BMAs more faithfully capture the intended behavioral mechanism and provide cleaner control over situated behavioral styles. Our results suggest that LLM personality-like tendencies are better understood not as abstract self-report traits, but as measurable and controllable behavioral modes grounded in concrete interaction contexts. Our code and data are available at https://github.com/lhz191/LLM-Behavioral-Personality.

Related papers

• From Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLMs [8.188989044347595]
  Existing studies of personality-related behavior in LLMs have primarily focused on outputs elicited under personality conditioning.<n>We introduce a framework for discovering, controlling, and validating trait-like representations in LLMs.<n>Our findings provide evidence that LLMs contain controllable trait-like representations linking internal states, situational expression, and behavioral outcomes.
  arXiv  Detail & Related papers  (2026-07-29T12:36:47Z)
• Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behavior [58.538864650160406]
  We run experiments across four behavioral tasks and 11 frontier LLMs.<n>We find that SR-behavior coherence exists but is selective.<n>These findings suggest that coarse personality frameworks may not be the best tools for testing deployment behavior.
  arXiv  Detail & Related papers  (2026-06-10T22:28:53Z)
• LLMs Reading the Rhythms of Daily Life: Aligned Understanding for Behavior Prediction and Generation [53.62804271492357]
  Large language models (LLMs) offer a promising direction due to their semantic richness, strong interpretability, and generative capabilities.<n>We propose Behavior Understanding Alignment (BUA), a novel framework that integrates LLMs into human behavior modeling through a structured curriculum learning process.<n>BUA employs sequence embeddings from pretrained behavior models as alignment anchors and guides the LLM through a three-stage curriculum, while a multi-round dialogue setting introduces prediction and generation capabilities.
  arXiv  Detail & Related papers  (2026-04-26T07:34:37Z)
• Linear Personality Probing and Steering in LLMs: A Big Five Study [0.7933052462113936]
  We investigate whether linear directions aligned with the Big Five personality traits can be used for probing and steering model behavior.<n>Our results suggest that linear directions aligned with trait-scores are effective probes for personality detection.
  arXiv  Detail & Related papers  (2025-12-19T14:41:09Z)
• Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusing [25.77984485421331]
  We investigate how Big Five personality profiles in LLMs influence priming in relevance labeling.<n>Our results show that certain profiles, such as High Openness and Low Neuroticism, consistently reduce priming susceptibility.<n>The most effective personality in mitigating priming may vary across models and task types.
  arXiv  Detail & Related papers  (2025-11-29T08:37:51Z)
• The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs [60.15472325639723]
  Personality traits have long been studied as predictors of human behavior.<n>Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems.
  arXiv  Detail & Related papers  (2025-09-03T21:27:10Z)
• Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm [57.00627691433355]
  We frame agent behavior steering as a model editing task, which we term Behavior Editing.<n>We introduce BehaviorBench, a benchmark grounded in psychological moral theories.<n>We demonstrate that Behavior Editing can be used to promote ethical and benevolent behavior or, conversely, to induce harmful or malicious behavior.
  arXiv  Detail & Related papers  (2025-06-25T16:51:51Z)
• LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model [58.887561071010985]
  Personality detection aims to detect one's personality traits underlying in social media posts. Most existing methods learn post features directly by fine-tuning the pre-trained language models. We propose a large language model (LLM) based text augmentation enhanced personality detection model.
  arXiv  Detail & Related papers  (2024-03-12T12:10:18Z)
• UPLex: Fine-Grained Personality Control in Large Language Models via Unsupervised Lexical Modulation [52.043831554626685]
  Personality is a crucial factor that shapes human communication patterns, thereby regulating the personalities of large language models (LLMs)<n>We propose UPLex, a method that uses an Unsupervisedly-Built personalized lexicon (UPL) during the decoding phase to manipulate LLM's personality traits.<n>UPLex can be constructed from a newly built situational judgment test dataset in an unsupervised fashion, and used to modulate the personality expression of LLMs.
  arXiv  Detail & Related papers  (2023-10-25T12:16:33Z)
• Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts [9.399159332152013]
  This study investigates the behaviors of Large Language Models (LLMs) when faced with conflicting prompts versus their internal memory. This will help to understand LLMs' decision mechanism and also benefit real-world applications, such as retrieval-augmented generation (RAG)
  arXiv  Detail & Related papers  (2023-09-29T17:26:03Z)

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.