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violation spans

Violation spans are specific contiguous segments, words, or phrases within a piece of text that directly cause the content to breach an established rule, policy, or safety guideline. In automated content moderation and natural language processing, identifying these exact causal text segments enables systems to pinpoint the precise source of non-compliance, such as toxic language, misleading claims, or policy-infringing statements, rather than assigning a general classification label to an entire document. Extracting violation spans supports interpretable decision-making, assists human reviewers in quickly locating infractions, and allows for targeted text editing to replace objectionable wording with compliant alternatives while preserving the surrounding benign context.

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Disarming Strategic Text: Span-Aware Counterfactuals for Robust Content Moderation

Disarming Strategic Text: Span-Aware Counterfactuals for Robust Content Moderation

Hardik Meisheri, Muhammad Zaid Hassan, Swati Tiwari, Puneet Mangla, Samarth Bharadwaj, Karthik Sankaranarayanan, Amit Singh

OrganizationsManipal Institute of TechnologyMicrosoft

Why you should read this

Presents a span-aware counterfactual data augmentation framework that isolates causal violation spans using multi-LLM consensus and rewrites them into policy-compliant hard negatives to defend content moderation classifiers against adversarial text manipulation.

Machine learning systems deployed in the wild must operate reliably despite unreliable inputs, whether arising from distribution shifts, adversarial manipulation, or strategic behavior by users. Content moderation is a prime example: violators deliberately exploit euphemisms, obfuscations, or benign co-occurrence patterns to evade detection, creating unreliable supervision signals for classifiers. We present a span-aware augmentation framework that generates high-quality counterfactual hard negatives to improve robustness under such conditions. Our pipeline combines (i) multi-LLM agreement to extract causal violation spans, (ii) policy-guided rewrites of those spans into compliant alternatives, and (iii) validation via re-inference to ensure only genuine label-flipping counterfactuals are retained. Across real-world ad moderation and toxic comment datasets, this approach consistently reduces spurious correlations and improves robustness to adversarial triggers, with PRAUC gains of up to +6.3 points. We further show that augmentation benefits peak at task-dependent ratios, underscoring the importance of balance in reliable learning. These findings highlight span-aware counterfactual augmentation as a practical path toward reliable ML from strategically manipulated and unreliable text data.

Added

2026-09-29