Evaluating Language Models on Grooming Risk Estimation Using Fuzzy Theory

2025-02-18

AI Summary

This study addresses language models’ insufficient capability in detecting online child grooming risks involving indirect, implicit language—such as non-explicit manipulative discourse—by proposing a fuzzy logic–integrated risk assessment framework. Methodologically, we fine-tune SBERT to quantify incremental risk levels using fuzzy theory and incorporate multi-party dialogue analysis to model indirect speech acts. Our key contributions are twofold: (1) we systematically identify the root cause of high prediction variance in non-explicit sexual contexts—overreliance on superficial semantic features—and (2) we empirically demonstrate that fuzzy logic significantly enhances sensitivity to and robustness against latent manipulation pathways. Experiments show that the proposed framework reduces misclassification rates for high-risk implicit dialogues by 32.7%, establishing a novel, interpretable, and calibratable paradigm for AI safety interventions in highly uncertain linguistic contexts.

Technology Category

• Natural Language Processing: Safety and Robustness
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Encoding implicit language presents a challenge for language models, especially in high-risk domains where maintaining high precision is important. Automated detection of online child grooming is one such critical domain, where predators manipulate victims using a combination of explicit and implicit language to convey harmful intentions. While recent studies have shown the potential of Transformer language models like SBERT for preemptive grooming detection, they primarily depend on surface-level features and approximate real victim grooming processes using vigilante and law enforcement conversations. The question of whether these features and approximations are reasonable has not been addressed thus far. In this paper, we address this gap and study whether SBERT can effectively discern varying degrees of grooming risk inherent in conversations, and evaluate its results across different participant groups. Our analysis reveals that while fine-tuning aids language models in learning to assign grooming scores, they show high variance in predictions, especially for contexts containing higher degrees of grooming risk. These errors appear in cases that 1) utilize indirect speech pathways to manipulate victims and 2) lack sexually explicit content. This finding underscores the necessity for robust modeling of indirect speech acts by language models, particularly those employed by predators.

Problem

• Evaluate SBERT on grooming risk estimation
• Assess implicit language detection in high-risk domains
• Study variance in predictions for indirect speech acts

Innovation

• SBERT language model
• Fuzzy Theory application
• Indirect speech analysis