# Ontologically Faithful Generation of Non-Player Character Dialogues

arXiv: https://arxiv.org/abs/2212.10618 · HTML: https://mlsanity.com/paper/2212.10618/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 192 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 28 of 28)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Explaining Answers with Entailment Trees](https://mlsanity.com/paper/2104.08661/) (2021)
- [Training Millions of Personalized Dialogue Agents](https://mlsanity.com/paper/1809.01984/) (2018)
- [Learning to Speak and Act in a Fantasy Text Adventure Game](https://mlsanity.com/paper/1903.03094/) (2019)
- [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019)
- [doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset](https://mlsanity.com/paper/2011.06623/) (2020)
- [ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons](https://mlsanity.com/paper/1909.03087/) (2019)
- [STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation](https://mlsanity.com/paper/2010.01717/) (2020)
- [Dialog Inpainting: Turning Documents into Dialogs](https://mlsanity.com/paper/2205.09073/) (2022)
- [Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence](https://mlsanity.com/paper/2210.07109/) (2022)
- [OTTers: One-turn Topic Transitions for Open-Domain Dialogue](https://mlsanity.com/paper/2105.13710/) (2021)
- [Dialogue Graph Modeling for Conversational Machine Reading](https://mlsanity.com/paper/2012.14827/) (2020)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems](https://mlsanity.com/paper/2104.09088/) (2021)
- [Generating Interactive Worlds with Text](https://mlsanity.com/paper/1911.09194/) (2019)
- [Telling Stories through Multi-User Dialogue by Modeling Character Relations](https://mlsanity.com/paper/2105.15054/) (2021)
- [Situated Dialogue Learning through Procedural Environment Generation](https://mlsanity.com/paper/2110.03262/) (2021)
- [Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation](https://mlsanity.com/paper/2205.09314/) (2022)
- [Facts2Story: Controlling Text Generation by Key Facts](https://mlsanity.com/paper/2012.04332/) (2020)
- [Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems](https://mlsanity.com/paper/2011.08243/) (2020)
- [TVStoryGen: A Dataset for Generating Stories with Character Descriptions](https://mlsanity.com/paper/2109.08833/) (2021)
- [Automated Story Generation as Question-Answering](https://mlsanity.com/paper/2112.03808/) (2021)
- [Leveraging Natural Supervision for Language Representation Learning and Generation](https://mlsanity.com/paper/2207.10617/) (2022)
- [Automating Direct Speech Variations in Stories and Games](https://mlsanity.com/paper/1708.09090/) (2017)

## Built on by (most cited first, 7 of 7)

- [RoleRAG: Enhancing LLM Role-Playing via Graph Guided Retrieval](https://mlsanity.com/paper/2505.18541/) (2025)
- [Tachikuma: Understading Complex Interactions with Multi-Character and Novel Objects by Large Language Models](https://mlsanity.com/paper/2307.12573/) (2023)
- [Interpreting User Requests in the Context of Natural Language Standing Instructions](https://mlsanity.com/paper/2311.09796/) (2023)
- [AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents](https://mlsanity.com/paper/2401.06509/) (2024)
- [Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain](https://mlsanity.com/paper/2401.06742/) (2024)
- [Shall We Play a Game? Language Models for Open-ended Wargames](https://mlsanity.com/paper/2509.17192/) (2025)
- [Controlling and Assessing Appropriate Persona Use in LLM-based Dialogue Generation](https://mlsanity.com/paper/2609.04676/) (2026)

## Abstract (arXiv)

We introduce a language generation task grounded in a popular video game environment. KNUDGE (KNowledge Constrained User-NPC Dialogue GEneration) requires models to produce trees of dialogue between video game characters that accurately reflect quest and entity specifications stated in natural language. KNUDGE is constructed from side quest dialogues drawn directly from game data of Obsidian Entertainment's The Outer Worlds, leading to real-world complexities in generation: (1) dialogues are branching trees as opposed to linear chains of utterances; (2) utterances must remain faithful to the game lore -- character personas, backstories, and entity relationships; and (3) a dialogue must accurately reveal new quest details to the human player. We report results for a set of neural generation models using supervised and in-context learning techniques; we find competent performance but room for future work addressing the challenges of creating realistic, game-quality dialogues.