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arXiv Computation and Language By Juneha Baek, Suhyeon Lee, Donghyuk Shin

How You Ask Shapes What You Get: A Theory-Seeded Measurement of Articulation in Advice-Seeking LLM Conversations

Read the original on arXiv Computation and Language →

The paper investigates how the way users phrase advice‑seeking requests—termed articulation—creates stable, measurable patterns distinct from the topics of the requests. By analyzing 16,447 prompts from public chat corpora, the authors identify a small set of latent articulation factors that consistently appear across datasets and splits. One key finding is a long‑form, information‑poor style that leads language models to give shorter, vaguer answers without seeking clarification, a pattern that persists across topics and prompt lengths.

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 Computation and Language.

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