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arXiv Machine Learning By James Ravi Kirkpatrick, Alexandru Radulescu, Rachel Katharine Sterken

Talking with Language Models

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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