Short-Text Conversation

Contains a base version (6.8million dialogues) and a large version (12.0 million dialogues). Source: A Large-Scale Chinese Short-Text Conversation Dataset

Data 1: Raw and Unlabeled; 2 million unlabeled replies from 17 Telegram channels. Data 2: Raw and Labeled; 15,076 replies from 17 Telegram channels categorized as no threat, judicial threat, and non-judicial threat.

This dataset contains synthetically generated discussions and annotations using exclusively Large Language Model (LLM) agents. Discussions are performed between randomly selected users, with a LLM moderator/facilitator following various facilitation strategies. Each LLM user and annotator use a distinct Sociodemographic Background. User-agents also have different roles when joining the discussion. Each discussion consists of 28 comments - 14 participant comments and 14 moderator interventions. Each comment is annotated by 10 different LLM annotators for toxicity and argument quality. Designed to analyze facilitation strategies and synthetic online discussion simulation but can also be used for finetuning LLM facilitators.