Authorship verification asks whether two given texts were written by the same author. Since supervised verification methods often generalize poorly across domains, the unsupervised Impostors Method, inspired by police lineups, reduces verification to in-domain authorship attribution. Using one text as a query, it hides the other among a sample of hard negatives and measures how reliably a style similarity function retrieves it at the top rank. Although widely applied in practice, the Impostors Method still suffers when no in-domain impostor documents are available. Resorting instead to sampling from open-domain collections provides much less control over confounding stylistic variables such as genre or register. Moreover, its statistical foundations remain largely unexplored compared with those of police lineups. In this paper, we recast the Impostors Method as a statistical hypothesis test, enabling a principled assessment of evidential strength without training a decision threshold. We further investigate large language models (LLMs) as a controllable source for generating in-domain impostor documents.

In this position paper, we advocate for the development of conversational technology that is inherently designed to support and facilitate argumentative processes. We argue that, at present, large language models (LLMs) are inadequate for this purpose, and we propose an ideal technology design aimed at enhancing argumentative skills. This involves re-framing LLMs as tools to exercise our critical thinking skills rather than replacing them. We introduce the concept of reasonable parrots that embody the fundamental principles of relevance, responsibility, and freedom, and that interact through argumentative dialogical moves. These principles and moves arise out of millennia of work in argumentation theory and should serve as the starting point for LLM-based technology that incorporates basic principles of argumentation.