Deflanderization for Game Dialogue: Balancing Character Authenticity
  with Task Execution in LLM-based NPCs
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).
68f04f9036f8b025381e18ec
Pasin Buakhaw
claimed_verified
2025-10-16T10:38:33.227Z
652a5d23b20d500447f7e80c
/avatars/eb555e48d36829aeaadc0bb97187df81.svg
Pasin Buakhaw
user
PasinB
68f04f9036f8b025381e18ed
Kun Kerdthaisong
claimed_verified
2025-10-16T06:39:08.330Z
63a83c5432ed73936eb8363e
https://cdn-avatars.huggingface.co/v1/production/uploads/63a83c5432ed73936eb8363e/8iJN61whs49R0bccdL7b5.jpeg
kun kerdthaisong
user
augustus2011
68f04f9036f8b025381e18ee
Phuree Phenhiran
68f04f9036f8b025381e18ef
Pitikorn Khlaisamniang
claimed_verified
2025-10-21T14:06:24.498Z
6624e0918fee5ba324f58bdd
/avatars/ae369d371174f2b2d3d425e03c7be32c.svg
Petanque
user
pitikorn32
68f04f9036f8b025381e18f0
Supasate Vorathammathorn
68f04f9036f8b025381e18f1
Piyalitt Ittichaiwong
68f04f9036f8b025381e18f2
Nutchanon Yongsatianchot
Deflanderization for Game Dialogue: Balancing Character Authenticity
  with Task Execution in LLM-based NPCs
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).
Participants in the CPDC 2025 used lightweight prompting and fine-tuned large models to achieve high rankings in task-oriented and context-aware dialogue challenges.
large language models LLMs non-player characters NPCs Commonsense Persona-Grounded Dialogue Challenge CPDC task-oriented dialogue context-aware dialogue lightweight prompting Deflanderization fine-tuned models Qwen3-14B supervised fine-tuning SFT Low-Rank Adaptation LoRA