提出机制导向的隐晦语言编码分类法，提升大模型检测敏感隐语能力。
Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection
- 按语言编码机制而非表达目的分类，抽象出可复用的生成模式。
- 在2000条社交平台数据上，准确率和F1值分别提升4.7%和5.4%。
- 适合内容审核系统开发者与对抗性语言研究者使用。
为规避社交媒体审查与监控，部分用户会创造间接语言表达（ILE）以隐藏敏感含义。这类表达表现为算法话术、委婉语及对抗性混淆，其形式随意图与语境而变，但均依赖重复出现的编码机制。本文提出一种机制导向的综合分类法，不关注沟通目标，而是聚焦意义编码与恢复的底层操作。通过将该分类法嵌入大模型提示，并在2000条人工标注的TikTok和Bluesky帖子上与四种现有分类法及无分类基线对比，结果表明该分类法在三种大模型上均实现最优文档级与片段级性能，准确率提升4.7%，F1值提升5.4%。实证显示，全面且机制导向的分类法是应对新兴隐语的稳定框架，也是内容审核的重要输入。注：本文包含可能冒犯或低俗的内容。
原文摘要 · Abstract (English)
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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