Itamar Rogel

Not in Kansas Anymore: Large Language Models and Player Community Building – Part 1

Hold onto your hats and get ready for a whirlwind of linguistic wizardry! Just like the mighty hurricane in the land of Oz, large language models (LLMs) have swooped into our lives, leaving us in awe of their power and potential. With a mighty sweep of their digital pens, they have rewritten the rules of language understanding and generation. From answering our burning questions to cracking jokes and spinning tales, LLMs have become our trusty companions on this incredible journey. And guess what? They’re not going anywhere! These enchanting language wizards are here to stay, bringing their boundless knowledge, creativity, and a touch of magic to our fingertips. So, buckle up and embrace the LLM adventure—it’s a tornado of innovation that’s set to revolutionize the way we interact, communicate, and explore the limitless realms of language!

Not in Kansas Anymore: Large Language Models and Player Community Building – Part 1

What is a large language model?


A large language model refers to a type of artificial intelligence model that has been trained on vast amounts of text data and has the ability to generate coherent and contextually relevant text. These models are based on deep learning architectures, particularly transformer models, and are characterized by their massive number of parameters, which can range from hundreds of millions to billions.


Large language models learn to understand the statistical patterns and relationships within the text they are trained on, allowing them to generate human-like responses and perform various natural language processing (NLP) tasks. They can comprehend and generate text at a sentence, paragraph, or even document level, and are often capable of handling diverse topics and domains.


These models are trained in a pre-training phase, where they learn from a large corpus of text data, and a fine-tuning phase, where they are further trained on specific tasks or domains. The pre-training phase helps the model acquire a broad understanding of language, while the fine-tuning phase enables it to specialize in particular NLP tasks such as text classification, question answering, language translation, and more.


Large language models have found applications in a wide range of fields, including virtual assistants, chatbots, content generation, language translation, sentiment analysis, and information retrieval, among others. They have significantly advanced the capabilities of natural language understanding and generation, making them powerful tools for processing and generating text-based content.


What are all the large language models out there?


You can trace the evolution of LLM models along a family tree of sorts, with many contributions along the way from various parties and contributors.


The most famous models in use today are probably the ones from OpenAI, who popularized the model through ChatGPT. These models include GPT-4 (currently their most advanced model), and its predecessors GPT-3, GPT-2, etc. GPT-3 has 175 billion parameters and can generate human-like text across a wide range of applications. Its predecessor, GPT-2, has 1.5 billion parameters and is still useful for various tasks, being capable of generating coherent and contextually relevant text.


Google has a widely-available LLM as well, Google Bard, which is now offered in a chat-based interface not unlike that of ChatGPT. Before that, they made significant contributions to the field, including models such as T5, BERT, and others.


Meta (Facebook), while not yet offering a consumer-facing chat-based interaction experience, has also made significant contributions to the field, culminating in their LLaMA model.


Other players in the field include Anthropic (with an LLM named Claude), AI21 Labs, and others. The overarching trend in the field to keep in mind is the gradual transition from innovation “in the open”, with discoveries and development fully shared as research papers and open source code, to a more competitive closed source model, as the different companies try to create moats around their IP and the business becomes more competitive.


What types of large language models can benefit the video game industry?


Large language models can bring several benefits to the video game industry. Here are some types of large language models that can be particularly useful:


Game Dialogue Generation: Large language models can generate realistic and engaging dialogue for non-player characters (NPCs) in video games. These models can create dynamic and contextually appropriate responses based on player interactions, enhancing the overall immersion and storytelling experience.


Game Testing and Quality Assurance: Language models can assist in game testing and quality assurance by automatically generating test cases, providing simulated player feedback, and identifying potential issues or bugs in game mechanics, user interfaces, or dialogue systems.


Procedural Content Generation: Language models can generate diverse and unique game content, such as levels, quests, items, and characters. By leveraging the model’s creativity and ability to understand player preferences, developers can create procedurally generated game elements that offer endless possibilities and increase re-playability.


Player Support and Chatbots: Large language models can be employed as virtual assistants or chatbots to provide personalized player support, answer frequently asked questions, and assist players with in-game challenges. These models can understand and respond to player queries, helping to enhance the player experience and reduce the workload on support teams.


Game Design Assistance: Language models can aid game designers by providing insights, suggestions, and feedback on game mechanics, level design, character development, and narrative elements. They can analyze player feedback, game reviews, and industry trends to assist in refining game designs and making data-driven decisions.


Localization and Translation: Large language models can assist in the localization and translation of games by providing automated translation suggestions, improving the accuracy and efficiency of the localization process. This can help game developers reach wider audiences and deliver localized experiences in multiple languages.


It’s important to note that while large language models offer valuable capabilities, their implementation should be done thoughtfully, ensuring ethical considerations, maintaining data privacy, and considering potential biases in the generated content.


How can large language models assist player-facing teams, such as player support teams or player community teams, in ensuring the best player experiences?


Just like the wise and compassionate characters from the Wizard of Oz, LLMs offer their exceptional abilities to assist teams in ensuring the most extraordinary player experiences. As if armed with the brains, heart, and courage of our favorite companions, LLMs lend their expertise to player support teams, equipping them with instant knowledge and solutions to address player concerns with unrivaled precision and empathy. Meanwhile, player community teams find solace in the LLMs’ linguistic magic, enabling them to foster vibrant and inclusive communities where players can express themselves freely and connect on a deeper level. Together with LLMs, these player-facing teams embark on an adventure down the yellow brick road, leading players to a realm of unforgettable gaming experiences, where every interaction is guided by the wisdom and enchantment of these remarkable language models.

An illustration that represents the yellow path and the entrance of Oz

Efficient and Accurate Communication: Large language models can help automate and streamline communication processes between player-facing teams and players. They can generate responses to common inquiries, provide information about game features or updates, and offer troubleshooting guidance. This enables teams to handle a larger volume of player inquiries efficiently while maintaining accuracy and consistency in their responses.


Natural Language Understanding: Language models can analyze and understand player feedback, including support tickets, forum posts, social media comments, and reviews. By processing and interpreting this data, the models can identify recurring issues, sentiment patterns, and player preferences. This information helps teams prioritize their efforts, address player concerns effectively, and make data-driven decisions to improve the player experience.


Enhanced Player Support: Large language models can act as virtual assistants to player support teams, providing real-time assistance in answering common player queries. They can offer instant responses, suggestions for troubleshooting, or even escalate complex issues to human agents when necessary. This improves response times, reduces player frustration, and enhances the overall support experience.


Community Engagement and Moderation: Language models can assist player community teams in managing online forums, social media platforms, and other community channels. They can help moderate conversations, detect and flag inappropriate content, and provide guidance on community guidelines. By maintaining a positive and inclusive environment, player communities can thrive, fostering engagement, collaboration, and a sense of belonging among players.


Content Generation and Personalization: Large language models can aid in generating personalized content for players, such as in-game dialogues, narratives, and quests. By understanding player preferences and behavior, the models can create tailored experiences that resonate with individual players. This customization enhances immersion, engagement, and overall satisfaction.


By leveraging the capabilities of large language models, player-facing teams can efficiently handle player inquiries, gain valuable insights from player feedback, provide timely support, foster vibrant communities, and deliver personalized experiences. Ultimately, these efforts contribute to ensuring the best player experiences in the video game industry.


On part 2 of this series, we’ll deep dive into how large language models can contribute to player sentiment analysis and guidelines on how to practically use LLMs to create the best player community experience. Stay tuned!