{"ID":2890613,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19470","arxiv_id":"2507.19470","title":"Conversations Gone Awry, But Then? Evaluating Conversational Forecasting Models","abstract":"We often rely on our intuition to anticipate the direction of a conversation. Endowing automated systems with similar foresight can enable them to assist human-human interactions. Recent work on developing models with this predictive capacity has focused on the Conversations Gone Awry (CGA) task: forecasting whether an ongoing conversation will derail. In this work, we revisit this task and introduce the first uniform evaluation framework, creating a benchmark that enables direct and reliable comparisons between different architectures. This allows us to present an up-to-date overview of the current progress in CGA models, in light of recent advancements in language modeling. Our framework also introduces a novel metric that captures a model's ability to revise its forecast as the conversation progresses.","short_abstract":"We often rely on our intuition to anticipate the direction of a conversation. Endowing automated systems with similar foresight can enable them to assist human-human interactions. Recent work on developing models with this predictive capacity has focused on the Conversations Gone Awry (CGA) task: forecasting whether an...","url_abs":"https://arxiv.org/abs/2507.19470","url_pdf":"https://arxiv.org/pdf/2507.19470v1","authors":"[\"Son Quoc Tran\",\"Tushaar Gangavarapu\",\"Nicholas Chernogor\",\"Jonathan P. Chang\",\"Cristian Danescu-Niculescu-Mizil\"]","published":"2025-07-25T17:55:13Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.HC\"]","methods":"[\"Language Model\"]","has_code":false}