{"ID":2869037,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16437","arxiv_id":"2509.16437","title":"SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversations","abstract":"Empathy is increasingly recognized as a key factor in human-AI communication, yet conventional approaches to \"digital empathy\" often focus on simulating internal, human-like emotional states while overlooking the inherently subjective, contextual, and relational facets of empathy as perceived by users. In this work, we propose a human-centered taxonomy that emphasizes observable empathic behaviors and introduce a new dataset, Sense-7, of real-world conversations between information workers and Large Language Models (LLMs), which includes per-turn empathy annotations directly from the users, along with user characteristics, and contextual details, offering a more user-grounded representation of empathy. Analysis of 695 conversations from 109 participants reveals that empathy judgments are highly individualized, context-sensitive, and vulnerable to disruption when conversational continuity fails or user expectations go unmet. To promote further research, we provide a subset of 672 anonymized conversation and provide exploratory classification analysis, showing that an LLM-based classifier can recognize 5 levels of empathy with an encouraging average Spearman $ρ$=0.369 and Accuracy=0.487 over this set. Overall, our findings underscore the need for AI designs that dynamically tailor empathic behaviors to user contexts and goals, offering a roadmap for future research and practical development of socially attuned, human-centered artificial agents.","short_abstract":"Empathy is increasingly recognized as a key factor in human-AI communication, yet conventional approaches to \"digital empathy\" often focus on simulating internal, human-like emotional states while overlooking the inherently subjective, contextual, and relational facets of empathy as perceived by users. In this work, we...","url_abs":"https://arxiv.org/abs/2509.16437","url_pdf":"https://arxiv.org/pdf/2509.16437v1","authors":"[\"Jina Suh\",\"Lindy Le\",\"Erfan Shayegani\",\"Gonzalo Ramos\",\"Judith Amores\",\"Desmond C. Ong\",\"Mary Czerwinski\",\"Javier Hernandez\"]","published":"2025-09-19T21:32:24Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\",\"LoRA\"]","has_code":false}