arXiv cs.CL· 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)·· 11 小时前AI 评分31
LLM 小说角色名集中问题研究：测量名字偏移与缓解重复
Character-Name Concentration in LLM Fiction: Measuring Displacement and Mitigating Repetition
AI 导读
研究提出免微调的 speculative naming 方法，将推测解码的草稿-验证-拒绝范式从 token 迁移到角色名，通过 verbalized sampling 生成候选名字集并用 SlopTracker 打分筛选。
正文 · 原文
Abstract:Character names in large language model (LLM) fiction concentrate on a handful of recurrent names such as 'Elara' and 'Elias', a measurable face of AI slop. Existing fixes either require logit access or retraining, or operate per request and cannot control concentration across a corpus. How much prompt-level strategies reduce concentration, and at what cost to name naturalness, has not been systematically measured. We present an empirical study of name-concentration control across eight strategies and introduce speculative naming, a fine-tuning-free method that adapts the draft-verify-reject paradigm of speculative decoding from tokens to names. The method requests candidate name-sets through verbalized sampling, scores them against a SlopTracker that combines a per-model fingerprint with a runtime counter, and selects through a temperature-controlled rule or its strict variant (HARD). We use 20 LLMs to generate 44,782 stories in four languages from text and visual stimuli (88,919 named characters), and measure concentration with a list-free collision metric (Slop Ratio, SR) decomposed into known and emergent components. List-based strategies do not remove slop: static banlists displace concentration to emergent names and more than halve the share of attested real names. Speculative naming removes it: the sampling scaffold alone cuts SR by 89% on average, the dynamic tracker brings the total reduction to 95%, mean collision probability falls from 7.3% to 0.4%, and known-name collisions hold at zero while naturalness stays close to baseline (42-69% attested by language) and within-story collisions drop from 4.3% to 0.2%. The strategy ranking is stable across languages, model families, and name-counting conventions. Name concentration is thus controllable at the corpus level without logit access or retraining; broader creative diversity beyond naming remains open.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.01284 [cs.CL] |
| (or arXiv:2611.01284v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.01284 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alejandro Mozo Quesada [view email]
[v1]
Sat, 31 Oct 2026 21:36:29 UTC (349 KB)
来源：arXiv cs.CL · arxiv.org