PIPPA: A Partially Synthetic Conversational Dataset

Tear Gosling, Alpin Dale, Yinhe Zheng
8/11/2023
cs.CL

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Abstract

With the emergence of increasingly powerful large language models, there is a burgeoning interest in leveraging these models for casual conversation and role-play applications. However, existing conversational and role-playing datasets often fail to capture the diverse and nuanced interactions typically exhibited by real-world role-play participants. To address this limitation and contribute to the rapidly growing field, we introduce a partially-synthetic dataset named PIPPA (Personal Interaction Pairs between People and AI). PIPPA is a result of a community-driven crowdsourcing effort involving a group of role-play enthusiasts. The dataset comprises over 1 million utterances that are distributed across 26,000 conversation sessions and provides a rich resource for researchers and AI developers to explore and refine conversational AI systems in the context of role-play scenarios.

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@article{gosling2023pippa,
  title  = {PIPPA: A Partially Synthetic Conversational Dataset},
  author = {Tear Gosling and Alpin Dale and Yinhe Zheng},
  year   = {2023},
  eprint = {2308.05884},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.05884v1}
}

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