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MCPDial: A Minecraft Persona-driven Dialogue Dataset

Seyed Hossein Alavi, Sudha Rao, Ashutosh Adhikari, Gabriel A DesGarennes, Akanksha Malhotra, Chris Brockett, Mahmoud Adada, Raymond T. Ng, Vered Shwartz, Bill Dolan

TL;DR

This work proposes a novel approach that uses large language models (LLMs) to generate persona-driven conversations between Players and Non-Player Characters (NPC) in games and introduces the Minecraft Persona-driven Dialogue dataset (MCPDial), starting with a small seed of expert-written conversations.

Abstract

We propose a novel approach that uses large language models (LLMs) to generate persona-driven conversations between Players and Non-Player Characters (NPC) in games. Showcasing the application of our methodology, we introduce the Minecraft Persona-driven Dialogue dataset (MCPDial). Starting with a small seed of expert-written conversations, we employ our method to generate hundreds of additional conversations. Each conversation in the dataset includes rich character descriptions of the player and NPC. The conversations are long, allowing for in-depth and extensive interactions between the player and NPC. MCPDial extends beyond basic conversations by incorporating canonical function calls (e.g. "Call find a resource on iron ore") between the utterances. Finally, we conduct a qualitative analysis of the dataset to assess its quality and characteristics.

MCPDial: A Minecraft Persona-driven Dialogue Dataset

TL;DR

This work proposes a novel approach that uses large language models (LLMs) to generate persona-driven conversations between Players and Non-Player Characters (NPC) in games and introduces the Minecraft Persona-driven Dialogue dataset (MCPDial), starting with a small seed of expert-written conversations.

Abstract

We propose a novel approach that uses large language models (LLMs) to generate persona-driven conversations between Players and Non-Player Characters (NPC) in games. Showcasing the application of our methodology, we introduce the Minecraft Persona-driven Dialogue dataset (MCPDial). Starting with a small seed of expert-written conversations, we employ our method to generate hundreds of additional conversations. Each conversation in the dataset includes rich character descriptions of the player and NPC. The conversations are long, allowing for in-depth and extensive interactions between the player and NPC. MCPDial extends beyond basic conversations by incorporating canonical function calls (e.g. "Call find a resource on iron ore") between the utterances. Finally, we conduct a qualitative analysis of the dataset to assess its quality and characteristics.

Paper Structure

This paper contains 21 sections, 2 figures, 6 tables.

Figures (2)

  • Figure 1: Example persona descriptions and the beginning of the automatically generated conversation from our dataset.
  • Figure 2: An overview of our conversation generation approach.