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Dynamic Personality Adaptation in Large Language Models via State Machines

Leon Pielage, Ole Hätscher, Mitja Back, Bernhard Marschall, Benjamin Risse

TL;DR

Results demonstrate that the system successfully adapts its personality state to user inputs, but also influences user behavior, thereby facilitating de-escalation training and demonstrating the feasibility of modular, personality-adaptive architectures for education, customer support, and broader human-computer interaction.

Abstract

The inability of Large Language Models (LLMs) to modulate their personality expression in response to evolving dialogue dynamics hinders their performance in complex, interactive contexts. We propose a model-agnostic framework for dynamic personality simulation that employs state machines to represent latent personality states, where transition probabilities are dynamically adapted to the conversational context. Part of our architecture is a modular pipeline for continuous personality scoring that evaluates dialogues along latent axes while remaining agnostic to the specific personality models, their dimensions, transition mechanisms, or LLMs used. These scores function as dynamic state variables that systematically reconfigure the system prompt, steering behavioral alignment throughout the interaction.We evaluate this framework by operationalizing the Interpersonal Circumplex (IPC) in a medical education setting. Results demonstrate that the system successfully adapts its personality state to user inputs, but also influences user behavior, thereby facilitating de-escalation training. Notably, the scoring pipeline maintains comparable precision even when utilizing lightweight, fine-tuned classifiers instead of large-scale LLMs. This work demonstrates the feasibility of modular, personality-adaptive architectures for education, customer support, and broader human-computer interaction.

Dynamic Personality Adaptation in Large Language Models via State Machines

TL;DR

Results demonstrate that the system successfully adapts its personality state to user inputs, but also influences user behavior, thereby facilitating de-escalation training and demonstrating the feasibility of modular, personality-adaptive architectures for education, customer support, and broader human-computer interaction.

Abstract

The inability of Large Language Models (LLMs) to modulate their personality expression in response to evolving dialogue dynamics hinders their performance in complex, interactive contexts. We propose a model-agnostic framework for dynamic personality simulation that employs state machines to represent latent personality states, where transition probabilities are dynamically adapted to the conversational context. Part of our architecture is a modular pipeline for continuous personality scoring that evaluates dialogues along latent axes while remaining agnostic to the specific personality models, their dimensions, transition mechanisms, or LLMs used. These scores function as dynamic state variables that systematically reconfigure the system prompt, steering behavioral alignment throughout the interaction.We evaluate this framework by operationalizing the Interpersonal Circumplex (IPC) in a medical education setting. Results demonstrate that the system successfully adapts its personality state to user inputs, but also influences user behavior, thereby facilitating de-escalation training. Notably, the scoring pipeline maintains comparable precision even when utilizing lightweight, fine-tuned classifiers instead of large-scale LLMs. This work demonstrates the feasibility of modular, personality-adaptive architectures for education, customer support, and broader human-computer interaction.
Paper Structure (40 sections, 2 equations, 5 figures, 4 tables)

This paper contains 40 sections, 2 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Personality Dimensions
  5. Message Analyzer
  6. Prompt Based Personality Mapping
  7. Data Generation
  8. Fine-tuned Personality Mappings
  9. Personality Model
  10. State Machine
  11. Transition Probabilities
  12. Generation
  13. Evaluation
  14. Analyzer Comparison and Fine-tuning
  15. User Study
  16. ...and 25 more sections

Figures (5)

  • Figure 1: The analysis of incoming messages and the selection of a personality prompt happen per dimension. Together with the role description and the message protocol, these provide the LLM-context.
  • Figure 2: A user message is analyzed, resulting in scores according to a specific personality dimension. These scores can (A) either be used directly to update the assistant personality or (B) be used to update a separate user personality model, which can then be used to update the assistant personality.
  • Figure 3: Steps to generate a reply to an incoming user message.
  • Figure 4: Evolution of the current personality of the user and the virtual patient (assistant) over time. (a)+(b): Mean evolution for all 40 participants. (c)+(d): A single long interaction.
  • Figure B.1: Various visualizations support the configuration of new assistant personalities and the creation of scenarios.