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arXiv Computation and Language By Alejandro Mozo Quesada (Universidad de M\'alaga, Spain), Sergio G\'alvez Rojas (Universidad de M\'alaga, Spain), Francisco L\'opez Valverde (Universidad de M\'alaga, Spain)

Character-Name Concentration in LLM Fiction: Measuring Displacement and Mitigating Repetition

Read the original on arXiv Computation and Language →

The paper investigates how large language models (LLMs) tend to reuse a small set of character names in fiction, a phenomenon termed ‘name slop’. It evaluates eight prompt‑level strategies and introduces a new fine‑tuning‑free method called speculative naming, which uses a SlopTracker and a draft‑verify‑reject loop to reduce name concentration. Across 20 LLMs and 44,782 stories, speculative naming cuts the Slop Ratio by 95 % while preserving name naturalness and within‑story collision rates.

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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