---
title: "Character-Name Concentration in LLM Fiction: Measuring Displacement and Mitigating Repetition"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.01284v1"
permalink: "https://feed.astralor.com/items/itm_f5b0f506dab6f76ff94c5e98"
published: "2026-10-31T21:36:29.000Z"
score: 60
channel: "tech"
tags: ["LLM","RAG","Character-Name Concentration","LLM Fiction","Measuring Displacement","Mitigating Repetition Character","Elara","Elias"]
---

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

## 速览

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 o…

## 关键点

- 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.

## 全文

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.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_f5b0f506dab6f76ff94c5e98