Year: 2026
Type: conference-paper
Abstract: As large language models (LLMs) are increasingly deployed as role-playing agents in educational and professional training simulations, their susceptibility to user-induced distraction threatens their pedagogical utility.We formalise goal-competing distraction as a controlled evaluation paradigm and introduce a simulation framework that captures both immediate reactions and multi-turn trajectories under targeted distraction, using an LLM-based user simulator.Building upon this framework, we evaluate agent behaviour across three models: Gemini-2.0-Flash,Llama-3.3-70B-Instruct, and Llama-3.1-8B-Instruct.Our findings reveal a critical trade-off dependent on model scale.While larger models tend to remain socially responsive and more frequently engage with distractor topics, the smaller model shows rigid goal adherence by resisting and rejecting distraction.Although redirection is the most common initial response, subsequent trajectories diverge substantially.The inclusion of explicit dialogue state demonstrates model-dependent effects, acting as a stabilising anchor for smaller models but providing limited benefit for larger ones.These results suggest that maintaining goal alignment in role-playing agents requires explicitly managing the trade-off between conversational responsiveness and goal adherence.
Authors: Dongxu Lu, Albert Gatt, Johan Jeuring
Institution: Utrecht University
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7214024756
FWCI:
Cites:
Cited by:
Related to: