Crafting Customisable Characters with LLM s: A Persona-Driven Role-Playing Agent Framework (2025)

Abstract: Large Language Models (LLMs) demonstrate remarkable ability to comprehend instructions and generate human-like text, enabling sophisticated agent simulation beyond basic behavior replication. However, the potential for creating freely customisable characters remains underexplored. We introduce the Customisable Conversation Agent Framework, which employs LLMs to simulate real-world characters through personalised characteristic feature injection, enabling diverse character creation according to user preferences.We propose the SimsConv dataset, comprising 68 customised characters and 13,971 multi-turn role-playing dialogues across 1,360 real-world scenes. Characters are initially customised using pre-defined elements (career, aspiration, traits, skills), then expanded through personal and social profiles. Building on this, we present SimsChat, a freely customisable role-playing agent incorporating various realistic settings and topic-specified character interactions.Experimental results on both SimsConv and WikiRoleEval datasets demonstrate SimsChat’s superior performance in maintaining character consistency, knowledge accuracy, and appropriate question rejection compared to existing models. Comprehensive ablation studies validate each component’s contribution to overall performance, with the pre-defined aspects framework and scene construction showing particularly significant impact. Our framework provides valuable insights for developing more accurate and customisable human simulacra.Our data and code are publicly available at https://github.com/Bernard-Yang/SimsChat.

## introduction recent advent of large language models (llms) @xcite @xcite has revolutionised the nlp landscape with their exceptional performance across * corresponding author a variety of tasks @xcite @xcite . among these developments, role-playing agents powered by llms have seen rapid expansion @xcite , gaining attention for their ability to engage users emotionally and emulate various characters. their applications span diverse contexts, from ai representations of fictional characters @xcite to ai non-player characters (npc) in video games @xcite . however, freely customisable role-playing agents remain relatively underexplored. prior research primarily focuses on simulating existing historic figures or characters from animation or fiction @xcite , which limits their potential due to fixed personalities and knowledge. in contrast, personalised role-playing agents offer the flexibility of being defined through multiple customisable aspects according to user preferences. in this work, we introduce the customisable conversation agent framework, which employs llms to simulate real-world characters through personalised characteristic feature injection. compared with agents that simulate existing characters, our method encompasses a wide range of characteristics, enabling the creation of diverse, customisable, and vivid real-world characters exhibiting various emotions, personalities, and detailed life experiences. our approach includes structured character generation with human verification to mitigate potential biases, ensuring high-quality and diverse character profiles. we first propose the simsconv dataset, comprising customised characters and multi-turn roleplaying dialogues in various simulated real-world scenes. as depicted in figure 1 , the development pipeline consists of character construction, scene construction, and interaction generation. characters are customised using comprehensive guidelines, followed by creation of various real-world scenes with detailed settings. characters then interact within these scenes, with specified emotions and conversation topics enhancing control over interactions. our comprehensive ablation studies demonstrate the crucial role of each component, with scene construction and pre-defined aspects framework providing particularly significant contributions to model performance. building on these foundations, we introduce simschat, a freely customisable role-playing agent that incorporates these realistic settings and topicspecified character interactions. experimental results on both simsconv and wikiroleeval datasets demonstrate simschat's superior performance in maintaining character consistency, knowledge accuracy, and appropriate question rejection compared to both general-purpose models like gpt-4 and specialised role-playing models, validating its effectiveness on both customised and unseen characters. our contributions can be summarised as follows: ## conclusion we present the customisable conversation agent framework for generating flexible role-playing agents. our work contributes the simsconv dataset and simschat agent, the first approach incorporating human preferences in simulating customisable characters with life-like dialogues. ablation studies confirm each component's value, particularly scene construction and pre-defined aspects. experimental results show significant improvements in character consistency and knowledge accuracy over existing models. our guidelines for creating preference-based customisable characters provide valuable insights for future role-playing agent development.