Sledgehammer or Scalpel? A Fine-grained Adaptive Framework for Implicit Hate Speech
Proposes FAID (Fine-grained Adaptive Implicit Hate speech Detection), a novel framework that classifies implicit hate speech into three fine-grained categories and applies tailored detection strategies for each Introduces three categories of implicit hate speech: Shallow (surface-identifiable intents), Targeted (malicious intent bound to concealed targets), and Context-Dependent (lacking background information) For Shallow samples, uses lightweight prompt-tuning for rapid classification; for Tar
Analysis
TL;DR
- Proposes FAID (Fine-grained Adaptive Implicit Hate speech Detection), a novel framework that classifies implicit hate speech into three fine-grained categories and applies tailored detection strategies for each
- Introduces three categories of implicit hate speech: Shallow (surface-identifiable intents), Targeted (malicious intent bound to concealed targets), and Context-Dependent (lacking background information)
- For Shallow samples, uses lightweight prompt-tuning for rapid classification; for Targeted samples, designs knowledge augmentation to iteratively refine the model and reveal hidden targets; for Context-Dependent samples, employs an agentic framework that auto-generates prompts to evolve context and infer missing background
- Achieves significant improvements over SOTA baselines across four benchmark datasets
- Demonstrates that adaptive, fine-grained approaches outperform monolithic single-reasoning-process methods in implicit hate speech detection
Why It Matters
Implicit hate speech detection is a critical challenge for content moderation at scale, as these messages evade traditional keyword-based and even standard LLM-based filters. This work provides a practical framework that balances detection accuracy with computational efficiency by allocating resources proportionally to sample complexity—a key consideration for real-world deployment in online platforms.
Technical Details
- Three-tier categorization: Shallow (surface-level implicit hate), Targeted (hidden targets with malicious intent), and Context-Dependent (requires external background knowledge for interpretation)
- Adaptive pipeline: A two-stage architecture where an initial classifier routes samples to category-specific processing modules
- Shallow module: Lightweight prompt-tuning for fast, low-cost classification of easily identifiable implicit hate
- Targeted module: Knowledge augmentation mechanism that iteratively refines model understanding to surface concealed targets
- Context-Dependent module: Agentic framework with automatic prompt generation that evolves context, infers missing background information, and resolves ambiguous malicious intents
- Evaluation: Tested on four benchmark datasets, outperforming existing SOTA baselines
Industry Insight
- The adaptive resource-allocation strategy offers a blueprint for building cost-efficient moderation systems that avoid one-size-fits-all reasoning, potentially reducing inference costs by 30-50% on high-volume platforms
- The agentic approach for context-dependent detection could be generalized to other NLP tasks requiring external knowledge inference, such as misinformation detection and sarcasm identification
- Content moderation teams should consider moving beyond monolithic LLM pipelines toward fine-grained, category-aware architectures that match detection complexity to sample difficulty
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