# PIPPA: A Partially Synthetic Conversational Dataset

arXiv: https://arxiv.org/abs/2308.05884 · HTML: https://mlsanity.com/paper/2308.05884/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Learning to Speak and Act in a Fantasy Text Adventure Game](https://mlsanity.com/paper/1903.03094/) (2019)
- [A Large-Scale Chinese Short-Text Conversation Dataset](https://mlsanity.com/paper/2008.03946/) (2020)
- [Personalized Dialogue Generation with Diversified Traits](https://mlsanity.com/paper/1901.09672/) (2019)
- [Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models](https://mlsanity.com/paper/2304.13835/) (2023)
- [A Repository of Conversational Datasets](https://mlsanity.com/paper/1904.06472/) (2019)
- [FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information](https://mlsanity.com/paper/2305.01528/) (2023)

## Built on by (most cited first, 6 of 6)

- [The Oscars of AI Theater: A Survey on Role-Playing with Language Models](https://mlsanity.com/paper/2407.11484/) (2024)
- [Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue](https://mlsanity.com/paper/2409.08330/) (2024)
- [PingPong: A Benchmark for Role-Playing Language Models with User Emulation and Multi-Model Evaluation](https://mlsanity.com/paper/2409.06820/) (2024)
- [SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs](https://mlsanity.com/paper/2411.07965/) (2024)
- [CharacterBench: Benchmarking Character Customization of Large Language Models](https://mlsanity.com/paper/2412.11912/) (2024)
- [Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges](https://mlsanity.com/paper/2608.17605/) (2026)

## Abstract (arXiv)

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.