PLACES: Prompting Language Models for Social Conversation Synthesis

• DOI: 10.48550/arxiv.2302.03269
• Publication Date: 2023-01-01
• Authors: Maximillian Chen; Alexandros Papang...; Chenyang Tao; Seokhwan Kim; Andy Rosenbaum; Yang Liu; Yu Zhou; Dilek Hakkani‐Tür
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Computation and Language (cs.CL); Information Retrieval (cs.IR); Computer Science - Information Retrieval

Collecting high quality conversational data can be very expensive for most applications and infeasible others due to privacy, ethical, or similar concerns. A promising direction tackle this problem is generate synthetic dialogues by prompting large language models. In work, we use a small set of expert-written conversations as in-context examples synthesize social conversation dataset using prompting. We perform several thorough evaluations our compared human-collected conversations. This includes various dimensions with human evaluation directly on the synthesized conversations, interactive chatbots fine-tuned synthetically generated dataset. additionally demonstrate that approach generalizable multi-party providing potential create new tasks. Our were rated more favorably across all measured excerpts sampled from