Using Games to Learn How Large Language Models Work

• URL: http://arxiv.org/abs/2603.28374v1
• Date: Mon, 30 Mar 2026 12:40:52 GMT
• Title: Using Games to Learn How Large Language Models Work
• Abstract summary: We propose two games that demonstrate principles behind how large language models (LLMs) work and use data.<n>The first game, Learn Like an LLM, aims to convey that LLMs are trained to predict sequences of text based on a particular dataset.<n>The second game, Tag-Team Text Generation, focuses on teaching that LLMs generate text one word at a time, using both predicted probabilities of the data and randomness.
• Score: 1.3750624267664158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While artificial intelligence (AI) technology is becoming increasingly popular, its underlying mechanisms tend to remain opaque to most people. To address this gap, the field of AI literacy aims to develop various resources to teach people how AI systems function. Here we contribute to this line of work by proposing two games that demonstrate principles behind how large language models (LLMs) work and use data. The first game, Learn Like an LLM, aims to convey that LLMs are trained to predict sequences of text based on a particular dataset. The second game, Tag-Team Text Generation, focuses on teaching that LLMs generate text one word at a time, using both predicted probabilities of the data and randomness. While the games proposed are still in early stages and would benefit greatly from further discussion, we hope they can contribute to using game-based learning to teach about complex AI systems like LLMs.

Related papers

• Learning to Play Like Humans: A Framework for LLM Adaptation in Interactive Fiction Games [8.06073345741722]
  Interactive Fiction games (IF games) are where players interact through natural language commands.<n>This work presents a cognitively inspired framework that guides Large Language Models (LLMs) to learn and play IF games systematically.
  arXiv  Detail & Related papers  (2025-05-18T14:21:56Z)
• Towards Anthropomorphic Conversational AI Part I: A Practical Framework [49.62013440962072]
  We introduce a multi- module framework designed to replicate the key aspects of human intelligence involved in conversations.<n>In the second stage of our approach, these conversational data, after filtering and labeling, can serve as training and testing data for reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-28T03:18:39Z)
• GigaCheck: Detecting LLM-generated Content [72.27323884094953]
  In this work, we investigate the task of generated text detection by proposing the GigaCheck. Our research explores two approaches: (i) distinguishing human-written texts from LLM-generated ones, and (ii) detecting LLM-generated intervals in Human-Machine collaborative texts. Specifically, we use a fine-tuned general-purpose LLM in conjunction with a DETR-like detection model, adapted from computer vision, to localize AI-generated intervals within text.
  arXiv  Detail & Related papers  (2024-10-31T08:30:55Z)
• Grammar-based Game Description Generation using Large Language Models [12.329521804287259]
  Game Description Language (GDL) provides a standardized way to express diverse games in a machine-readable format.<n>This paper presents a novel framework that leverages Large Language Models (LLMs) to generate grammatically accurate game descriptions from natural language.
  arXiv  Detail & Related papers  (2024-07-24T16:36:02Z)
• LLaRA: Supercharging Robot Learning Data for Vision-Language Policy [56.505551117094534]
  We introduce LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as visuo-textual conversations.<n>First, we present an automated pipeline to generate conversation-style instruction tuning data for robots from existing behavior cloning datasets.<n>We show that a VLM finetuned with a limited amount of such datasets can produce meaningful action decisions for robotic control.
  arXiv  Detail & Related papers  (2024-06-28T17:59:12Z)
• A Complete Survey on LLM-based AI Chatbots [46.18523139094807]
  The past few decades have witnessed an upsurge in data, forming the foundation for data-hungry, learning-based AI technology. Conversational agents, often referred to as AI chatbots, rely heavily on such data to train large language models (LLMs) and generate new content (knowledge) in response to user prompts. This paper presents a complete survey of the evolution and deployment of LLM-based chatbots in various sectors.
  arXiv  Detail & Related papers  (2024-06-17T09:39:34Z)
• Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT [2.8000537365271367]
  Large language models (LLMs) have emerged as a vibrant research topic. LLMs face challenges in remembering events, incorporating new information, and addressing domain-specific issues or hallucinations. This article proposes a method for automatically constructing a Knowledge Graph from multiple data sources.
  arXiv  Detail & Related papers  (2024-04-14T16:34:31Z)
• Enhance Reasoning for Large Language Models in the Game Werewolf [15.730860371636336]
  This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module. Our framework is presented using a 9-player Werewolf game that demands dual-system reasoning. Experiments demonstrate the framework's effectiveness in deductive reasoning, speech generation, and online game evaluation.
  arXiv  Detail & Related papers  (2024-02-04T03:47:10Z)
• VILA: On Pre-training for Visual Language Models [74.08039416548209]
  We study the design options for VLM pre-training through step-by-step controllable comparisons. We build VILA, a Visual Language model family that consistently outperforms the state-of-the-art models.
  arXiv  Detail & Related papers  (2023-12-12T18:58:18Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models [70.82705830137708]
  We introduce Data-driven Instruction Augmentation for Language-conditioned control (DIAL) We utilize semi-language labels leveraging the semantic understanding of CLIP to propagate knowledge onto large datasets of unlabelled demonstration data. DIAL enables imitation learning policies to acquire new capabilities and generalize to 60 novel instructions unseen in the original dataset.
  arXiv  Detail & Related papers  (2022-11-21T18:56:00Z)
• AI in (and for) Games [0.9920773256693857]
  This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. On one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour. On the other hand, games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour.
  arXiv  Detail & Related papers  (2021-05-07T08:57:07Z)
• On the interaction between supervision and self-play in emergent communication [82.290338507106]
  We investigate the relationship between two categories of learning signals with the ultimate goal of improving sample efficiency. We find that first training agents via supervised learning on human data followed by self-play outperforms the converse.
  arXiv  Detail & Related papers  (2020-02-04T02:35:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.