# Deflanderization for Game Dialogue: Balancing Character Authenticity with Task Execution in LLM-based NPCs

arXiv: https://arxiv.org/abs/2510.13586 · HTML: https://mlsanity.com/paper/2510.13586/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [Personalized Language Modeling from Personalized Human Feedback](https://mlsanity.com/paper/2402.05133/) (2024)
- [Large Language Models are Diverse Role-Players for Summarization Evaluation](https://mlsanity.com/paper/2303.15078/) (2023)
- [PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives](https://mlsanity.com/paper/2305.02364/) (2023)
- [Large Language Models as User-Agents for Evaluating Task-Oriented-Dialogue Systems](https://mlsanity.com/paper/2411.09972/) (2024)
- [Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft](https://mlsanity.com/paper/2407.03460/) (2024)
- [LLM-Driven NPCs: Cross-Platform Dialogue System for Games and Social Platforms](https://mlsanity.com/paper/2504.13928/) (2025)
- [You Have Thirteen Hours in Which to Solve the Labyrinth: Enhancing AI Game Masters with Function Calling](https://mlsanity.com/paper/2409.06949/) (2024)
- [MCPDial: A Minecraft Persona-driven Dialogue Dataset](https://mlsanity.com/paper/2410.21627/) (2024)

## Abstract (arXiv)

The emergence of large language models (LLMs) has opened new opportunities for creating dynamic non-player characters (NPCs) in gaming environments, enabling both functional 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 evaluates 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).