arXiv:2607. 25620v1 Announce Type: new Abstract: Quattrociocchi and colleagues warn that the fluent outputs of large language models may allow linguistic plausibility to substitute for epistemic evaluation, producing the condition they call *Epistemia*: the experience of possessing knowledge without undertaking the practices through which judgment would ordinarily be warranted.
By Federico Cabitza, Gianluca Colombo
The article examines chatbots as partners in problem‑solving conversations, arguing that basic chatbots—comprising a large language model (LLM) and a simple interface—are multifaceted but cannot match human cognitive flexibility. Drawing on Aggregation Dynamics, Cognitive Linguistics, Neuropsychology, and Psychology, the authors describe how LLMs encode artificial metaphorical problem propagations from training data, which only partially imitate human thinking. They conclude that further LLM development will not yield true thinking partners, yet chatbots are widely used, making their understanding socially and politically important.
By S. F. M. van Vlijmen, H. D. Lethe jr
The paper argues that large language models (LLMs) should not be viewed as conversational partners but as sophisticated text-generating artifacts. It introduces the concept of an artifactual stance, framing human‑AI interaction as exchanges of candidate texts optimized for utility rather than meaningful utterances. By rejecting the illusion of dialogue, the authors suggest that philosophical concerns about identity, responsibility, and truth in AI interactions are misplaced, and instead focus should shift to the normative design and use of these artifacts.
By James Ravi Kirkpatrick, Alexandru Radulescu, Rachel Katharine Sterken
The article presents a minimal working model for large language model (LLM) systems, emphasizing four key distinctions—pretraining vs. deployment, distribution vs. samples, types of memory, and task competence vs. agency. Using this framework, it diagnoses six common misconceptions about LLMs (next‑token prediction, regression to the mean, training‑data regurgitation, model memory, alignment, and understanding), explaining what each misconception captures correctly, where it conflates distinctions, and the implications for evaluation, design, and governance. The model is applied to AI policy language, illustrating how policy can misrepresent these distinctions and offering a diagnostic toolkit to correct such errors.
By Zhicheng Lin
arXiv:2408.11827v2 Announce Type: replace
Abstract: Understanding how language models compose meaning from linguistic input remains a central problem in interpretability research. Mechanistic studies...
By Nura Aljaafari, Danilo S. Carvalho, Andr\'e Freitas
arXiv:2608. 13604v1 Announce Type: new Abstract: Detection of misunderstanding is an urgent problem to solve because communication has moved away from real-time, in-person interaction and is increasingly handled by AI-mediated channels.
By Babak Abbaschian