Deflanderization for Game Dialogue: Balancing Character Authenticity
  with Task Execution in LLM-based NPCs
Authors: name: Pasin Buakhaw, name: Kun Kerdthaisong, name: Phuree Phenhiran, name: Pitikorn Khlaisamniang, name: Supasate Vorathammathorn, name: Piyalitt Ittichaiwong, name: Nutchanon Yongsatianchot
Organization: Character-lab

The emergence of large language models (LLMs) has opened new opportunities
for cre- ating dynamic non-player characters (NPCs) in gaming environments,
enabling both func- tional task execution and persona-consistent dialogue
generation. In this paper, we (Tu_Character_lab) report our participation in
the Commonsense Persona-Grounded Dialogue Challenge (CPDC) 2025 Round 2, which
eval- uates agents across three tracks: task-oriented dialogue, context-aware
dialogue, and their integration. Our approach combines two complementary
strategies: (i) lightweight prompting techniques in the API track, including a
Deflanderization prompting method to suppress excessive role-play and improve
task fidelity, and (ii) fine-tuned large models in the GPU track, leveraging
Qwen3-14B with supervisedfinetuning (SFT) and Low-Rank Adaptation(LoRA). Our
best submissions ranked 2nd on Task 1, 2nd on Task 3 (API track), and 4th on
Task 3 (GPU track).