%0 Conference Proceedings %T PLACES: Prompting Language Models for Social Conversation Synthesis %A Chen, Maximillian %A Papangelis, Alexandros %A Tao, Chenyang %A Kim, Seokhwan %A Rosenbaum, Andy %A Liu, Yang %A Yu, Zhou %A Hakkani-Tur, Dilek %Y Vlachos, Andreas %Y Augenstein, Isabelle %S Findings of the Association for Computational Linguistics: EACL 2023 %D 2023 %8 May %I Association for Computational Linguistics %C Dubrovnik, Croatia %F chen-etal-2023-places %X Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising direction to tackle this problem is to generate synthetic dialogues by prompting large language models. In this work, we use a small set of expert-written conversations as in-context examples to synthesize a social conversation dataset using prompting. We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations. This includes various dimensions of conversation quality with human evaluation directly on the synthesized conversations, and interactive human evaluation of chatbots fine-tuned on the synthetically generated dataset. We additionally demonstrate that this prompting approach is generalizable to multi-party conversations, providing potential to create new synthetic data for multi-party tasks. Our synthetic multi-party conversations were rated more favorably across all measured dimensions compared to conversation excerpts sampled from a human-collected multi-party dataset. %R 10.18653/v1/2023.findings-eacl.63 %U https://aclanthology.org/2023.findings-eacl.63 %U https://doi.org/10.18653/v1/2023.findings-eacl.63 %P 844-868