"This research introduces Procedural Artificial Narrative using Generative AI (PANGeA), a structured approach for leveraging large language models (LLMs), guided by a game designer's high-level criteria, to generate narrative content for turn-based role-playing video games (RPGs)."

Full abstract: "This research introduces Procedural Artificial Narrative using Generative AI (PANGeA), a structured approach for leveraging large language models (LLMs), guided by a game designer's high-level criteria, to generate narrative content for turn-based role-playing video games (RPGs). Distinct from prior applications of LLMs used for video game design, PANGeA innovates by not only generating game level data (which includes, but is not limited to, setting, key items, and non-playable characters (NPCs)), but by also fostering dynamic, free-form interactions between the player and the environment that align with the procedural game narrative. The NPCs generated by PANGeA are personality-biased and express traits from the Big 5 Personality Model in their generated responses. PANGeA addresses challenges behind ingesting free-form text input, which can prompt LLM responses beyond the scope of the game narrative. A novel validation system that uses the LLM's intelligence evaluates text input and aligns generated responses with the unfolding narrative. Making these interactions possible, PANGeA is supported by a server that hosts a custom memory system that supplies context for augmenting generated responses thus aligning them with the procedural narrative. For its broad application, the server has a REST interface enabling any game engine to integrate directly with PANGeA, as well as an LLM interface adaptable with local or private LLMs. PANGeA's ability to foster dynamic narrative generation by aligning responses with the procedural narrative is demonstrated through an empirical study and ablation test of two versions of a demo game. These are, a custom, browser-based GPT and a Unity demo. As the results show, PANGeA holds potential to assist game designers in using LLMs to generate narrative-consistent content even when provided varied and unpredictable, free-form text input." Buongiorno, S., Klinkert, L. J., Chawla, T., Zhuang, Z., & Clark, C. (2024). PANGeA: Procedural Artificial Narrative using Generative AI for Turn-Based Video Games. arXiv preprint arXiv:2404.19721.

OpenAI unveils GPT-4o, a new flagship "omnimodel" capable of processing text, audio, and video. While it delivers big improvements in speed, cost, and reasoning ability, perhaps the most impressive is its new voice mode -- while the old version was a clunky speech --> text --> speech approach with tons of latency, the new model takes in audio directly and responds in kind, enabling real-time conversations with an eerily realistic voice, one that can recognize multiple speakers and even respond with sarcasm, laughter, and other emotional content of speech. Rumor has it Apple has neared a deal with the company to revamp an aging Siri, while the advance has clear implications for customer service, translation, education, and even virtual companions (or perhaps "lovers", as the allusions to Spike Jonze's Her, the Samantha-esque demo voice, and opening the door to mature content imply). Meanwhile, the offloading of most premium ChatGPT features to the free tier suggests something bigger coming down the pike.

Chatbot vs Chatbot The Chatbot Arena will randomly load two chatbots in answer to your prompt. You mark which one gives the better answer. The Arena uses these human responses to rank the top LLM chatbots on an ongoing basis. Over 1,000,000 prompts have been submitted and scored.

Regardless of whether you do the ranking or not, the Arena is a good way to get multiple answers to your single prompt.