---
title: "超越表面形式：面向机制的间接语言编码综合分类法——用于基于LLM的编码语言检测"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2606.27314v1"
permalink: "https://feed.astralor.com/items/itm_37061fc0b41ab11beb2725fe"
published: "2026-06-25T17:29:45.000Z"
score: 53
channel: "ai"
tags: ["LLM","内容审核","分类法","社交媒体","编码语言检测"]
---

# 超越表面形式：面向机制的间接语言编码综合分类法——用于基于LLM的编码语言检测

## 速览

社交媒体用户常使用间接语言表达（ILE）规避内容审核，如算法暗语、委婉语和对抗性混淆。现有分类法侧重于沟通意图，导致检测不稳定。本文提出一种面向机制的综合分类法，抽象出编码底层操作，并将其集成到LLM提示中。在2000条手动标注的TikTok和Bluesky帖子上，新分类法在文档级和跨度级性能均最强，准确率提升4.7%，F1提升5.4%，为检测新兴编码语言提供了稳定支架。

## 关键点

- 提出面向机制的间接语言表达（ILE）综合分类法，专注于编码的底层操作而非沟通意图。
- 将该分类法集成到LLM提示中，在2000条手动标注的社交媒体帖子上评估。
- 与四种现有分类法和无分类法基线相比，准确率提升4.7%，F1提升5.4%。
- 揭示了全面、面向机制的分类法作为检测新兴编码语言的稳定支架的重要性。

## 全文

To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive meanings. Such expressions surface as algospeak, euphemisms, and adversarial obfuscation, depending on intent and context, and they involve recurring encoding mechanisms. We propose a comprehensive, mechanism-oriented taxonomy of ILE that abstracts away from communicative goals and instead categorizes the underlying operations through which meaning is encoded and recovered. We evaluate the taxonomy by incorporating it into LLM prompts and comparing it with four existing taxonomies and a no-taxonomy baseline, using 2,000 manually annotated TikTok and Bluesky posts. The proposed taxonomy attains the strongest document- and span-level performance across the three LLMs, achieving an improvement of 4.7% in accuracy and 5.4% in F1 over the best-performing benchmark. The empirical results reveal the importance of a comprehensive, mechanism-oriented taxonomy as a stable scaffold for detecting emerging coded language and a useful input to content moderation. Disclaimer: This paper contains content that may be profane, vulgar, or offensive.

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