Papers with PII detection
CAPID: Context-Aware PII Detection for Question-Answering Systems (2026.eacl-srw)
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Mariia Ponomarenko, Sepideh Abedini, Masoumeh Shafieinejad, D. B. Emerson, Shubhankar Mohapatra, Xi He
| Challenge: | Existing approaches mainly redact all PII, disregarding the fact that some may be contextually relevant to the user’s question, resulting in a degradation of response quality. |
| Approach: | They propose a method that fine-tunes a locally owned small language model that filters sensitive information before it is passed to LLMs for QA. |
| Outcome: | The proposed approach outperforms baselines in span, relevance and type accuracy while preserving significantly higher utility under anonymization. |
SPY: Enhancing Privacy with Synthetic PII Detection Dataset (2025.naacl-srw)
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| Challenge: | Historically, Named Entity Recognition (NER) has been employed for PII detection, but PI I entities constitute a subset of NER entities. |
| Approach: | They propose to use Large Language Models to generate a synthetic dataset that emulates real-world PII scenarios and validate its quality. |
| Outcome: | The proposed dataset is validated and provides a benchmark for PII detection. |
Semantic Span Annotation: An Exploratory Study of LLM Annotation (2026.acl-srw)
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| Challenge: | Structured span extraction research is siloed by context length, annotation task, and domain . Identifying a span within a natural language text and affixing it with a semantic label has been considered a core task in NLP . |
| Approach: | They propose a framework for structured span annotation that integrates five datasets under a common JSONL format with character-level offsets. |
| Outcome: | The proposed framework can generalize across four domains under three prompting configurations. |
PII-Bench: Evaluating Query-Aware Privacy Protection Systems (2026.acl-long)
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| Challenge: | Existing models do not detect PII in user prompts, despite their convenience . current models show significant limitations in determining PI I query relevance . |
| Approach: | They propose a query-unrelated PII masking strategy and propose PIi-Bench . they propose 'quick-and-easy' PI I masking with a user query and context description . |
| Outcome: | The proposed model performs well in basic PII detection, but shows significant limitations in query relevance. |