Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection

2026-06-25 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

A new taxonomy of indirect linguistic encoding (ILE) has been proposed to improve the detection of camouflaged sensitive meanings used to evade social media moderation and surveillance. This mechanism-oriented taxonomy categorizes the underlying operations for encoding and recovering meaning, abstracting away from communicative goals like algospeak, euphemisms, or adversarial obfuscation. Evaluated against 2,000 manually annotated TikTok and Bluesky posts, the taxonomy was incorporated into LLM prompts and compared with four existing taxonomies and a no-taxonomy baseline. Across three different LLMs, the proposed taxonomy achieved the strongest document- and span-level performance, demonstrating a 4.7% improvement in accuracy and a 5.4% improvement in F1 score over the best-performing benchmark. This comprehensive approach provides a stable scaffold for detecting emerging coded language and serves as a useful input for content moderation systems.

Key takeaway

For AI Security Engineers or NLP Engineers developing content moderation systems, adopting a mechanism-oriented taxonomy for indirect linguistic encoding is crucial. Your LLM-based detection models can achieve significantly higher accuracy and F1 scores by integrating such a comprehensive framework into prompts. This approach provides a robust method to identify evolving coded language, enhancing the effectiveness of your moderation efforts against sophisticated obfuscation techniques.

Key insights

A mechanism-oriented taxonomy of indirect linguistic encoding significantly enhances LLM-based coded language detection.

Principles

• Categorize encoding operations, not communicative goals.
• Comprehensive taxonomies improve LLM detection performance.

Method

The method involves incorporating a mechanism-oriented taxonomy of indirect linguistic encoding into LLM prompts and evaluating its performance against existing taxonomies and baselines using manually annotated social media data.

In practice

• Integrate mechanism-oriented taxonomies into LLM prompts.
• Apply this taxonomy to improve content moderation systems.

Topics

• Indirect Linguistic Encoding
• LLM Prompts
• Coded Language Detection
• Content Moderation
• Social Media Analysis
• Taxonomy Design

Best for: AI Architect, AI Engineer, Research Scientist, AI Scientist, NLP Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.