A new arXiv paper analyzes public chatbot logs to understand how people write stories with large language models. The authors find that users repeatedly revise their prompts, tweaking characters and other story elements to explore different narrative directions. The work frames this behavior as navigation through a space of possible stories.
A new arXiv paper examines the trend of users consulting large language models as all-knowing authorities on subjective personal questions. The authors propose a typology of this behavior and LLM-based measurement methods, citing concerns about user autonomy and well-being. The work is categorized as a cross-listing on arXiv cs.AI.
A new arXiv preprint studies how users direct hostility, coercion, and adversarial pressure at conversational AI models, an area the authors say is often overlooked in favor of research on model-generated harms. The paper argues that understanding when and why such mistreatment happens is needed to correctly interpret model behavior and alignment drift. It appears under the cs.AI category as a new submission.