arXiv AI
Argumentation-Based Person-Tailored Storytelling for Older Adults: A Reflective Hybrid AI Architecture
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arXiv Computation and Language
Aug 24
ARGUS: Theory-of-Mind Guided Argument Generation with Strategy-Aware Planning and Knowledge Grounding
ARGUS is a new agent-based framework for persuasive argument generation that incorporates a Theory-of-Mind Reasoner to model audience beliefs and values. It uses a component-aware planner to break arguments into subtopics, assign rhetorical functions (logos, pathos, ethos, kairos), and guide evidence retrieval during planning. A refinement module iteratively addresses multi-dimensional weaknesses, and evaluations on three benchmarks show ARGUS outperforming strong baselines and effectively shifting resistant audience stances.
arXiv Computation and Language
Oct 12
Activating the Annotator: Operationalising Lived Experience in Data Annotation
The paper introduces AXLE, a pre‑annotation stage that uses a scenario vignette and guided conversation to generate a first‑person micro‑narrative for annotators. In a 2x2 study with 104 annotators on ambiguous hate‑speech items, AXLE increased identity resonance, sensitivity to harm, label entropy, and reduced inter‑annotator agreement, suggesting it surfaces plural perspectives rather than forcing consensus.
arXiv AI
Sep 28
Thinking Less to Simulate Better: Intuitive Prompting Improves LLM Agents Simulating Individual Social Media Reactions, Including Unfamiliar Content
The study evaluates how well language‑model agents can simulate individual social media reactions by comparing predictions under different prompt conditions. Eight Serbian participants’ reactions to 68 posts were recorded, and four language models were asked to predict these reactions using prompts that varied in profile content and instruction style. The results show that prompts emphasizing attitudinal content and intuitive, immediate responses yield the highest fidelity, outperforming demographic backstories and a crowd baseline, and suggesting that such agents could act as general‑purpose simulated users.
arXiv AI
Sep 16
Verifiable Social Reasoning for LLM Assistants
arXiv:2609.17496v1 Announce Type: new Abstract: LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (...
arXiv AI
Jun 8
Telling stories, making Hanzi: AI-assisted co-creation with elderly migrants in urban China
arXiv:2507. 01548v3 Announce Type: replace-cross Abstract: This paper explores how older migrants in urban China can record stories that everyday language and design often miss.
arXiv AI
Jun 6
A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing
arXiv:2606. 05330v1 Announce Type: cross Abstract: Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change.