Narrative and dialogue generation for video-games: A systematic mapping

Procedural content generation has the potential to increase a game’s replayability, reduce development costs and time, and tailor experiences to users. The generation of dialogues and narratives has gained increased interest thanks to the advance of Large Language Models (LLMs) and their potential to generate convincing texts about many subjects. However, many reported implementations have poor reception from users and face limitations. This study presents a systematic mapping of the literature on the use of LLMs for generating dialogues and narratives in digital games, aiming to discover the most used models, datasets, prompt elements, fine-tuning 1 strategies, integration methods in games, how each study was evaluated, and their main issues. Through an extensive search across multiple databases and the application of rigorous inclusion and exclusion criteria, we analyzed 55 articles that apply LLMs in gaming contexts. The most recurrent challenges include narrative incoherence, repetitiveness, memory limitations, and latency, all of which directly impact player immersion. The results reveal the predominance of Generative Pre-trained Transformer (GPT) family models and an increasing use of LLMs in game modifications and interactive environments. This work contributes to the field by providing a comprehensive overview of existing approaches, identifying research gaps, and suggesting future directions, such as the need for standardized evaluation methods, the development of robust solutions to address memory issues, and the importance of comparing LLM-generated content with human-created material.