---
title: "CAPID — paper by Ponomarenko et al. (2026)"
description: "Question-answering assistants often receive queries in which users mention personal details. 21 references, cited by 1 paper here."
authors: ["Mariia Ponomarenko", "Sepideh Abedini", "Masoumeh Shafieinejad", "D. B. Emerson", "Shubhankar Mohapatra", "Xi He"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)"
source: "https://aclanthology.org/2026.eacl-srw.23/"
doi: "10.18653/v1/2026.eacl-srw.23"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ponomarenko-2026-capid-context-aware-pii-detection/"
---
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# CAPID: Context-Aware PII Detection for Question-Answering Systems

**Authors:** Mariia Ponomarenko, Sepideh Abedini, Masoumeh Shafieinejad, D. B. Emerson, Shubhankar Mohapatra and Xi He · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-srw.23/) · [PDF](https://aclanthology.org/2026.eacl-srw.23.pdf) · [DOI 10.18653/v1/2026.eacl-srw.23](https://doi.org/10.18653/v1/2026.eacl-srw.23) · [OpenAlex](https://openalex.org/W7140109668)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary

Question-answering assistants often receive queries in which users mention personal details. Standard PII tools mask all of them, even when some details (such as a job or a health condition) are needed for a good answer. Sending raw queries to third-party LLMs is a privacy risk. CAPID addresses this by running a small, locally hosted language model that finds PII spans, assigns each one a type, and decides whether it matters for the question. Only the irrelevant spans are masked before the query goes to an external LLM.

No public dataset labels this kind of question-dependent relevance, so the authors built a synthetic one. GPT-4.1-mini and GPT-5 generated topics from pairs of PII types, then wrote contexts that mix relevant and peripheral personal details, followed by matching questions. Llama models fine-tuned on this data classified relevance better than GPT-4.1-mini on annotated real Reddit queries. Selective masking also kept answers more useful than full redaction. This shows that privacy protection and answer quality can be balanced.

## Key points

- **PII taxonomy:** 15 fine-grained types, including occupation, health, location, belief, code and appearance. Pairing 13 of these types gave 78 combinations, which expanded into 15,600 topic–subtopic pairs. After de-duplication, 11,663 unique (PIIType1, PIIType2, subtopic) triplets remained.
- **Models:** Llama-3.1-8B and Llama-3.2-3B were fine-tuned. The 8B LoRA model is released on Hugging Face as `ponoma16/capid-llama8b-lora`, and the code and dataset are on GitHub.
- **Relevance accuracy on Reddit:** the fine-tuned Llama-3.1-8B scored 0.7994, compared with 0.6896 for GPT-4.1-mini. GPT-4.1-mini over-predicted spans, giving high recall but lower precision.
- **Downstream utility:** answers were generated by GPT-4 and judged by both GPT-4 and Claude Sonnet 4.5. Masking only low-relevance PII was preferred over full masking in 0.73–0.80 of cases. The pretrained-SLM baseline of Ngong et al. (2025) reached only 0.43–0.58.
- **Limitations:** performance drops on long contexts and on questions without cues about what information is needed. Relevance labels are binary rather than continuous sensitivity scores, and the method does not apply expert, domain-specific rules (e.g., legal or medical).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [University of Waterloo](https://md.research.elamur.ai/free/en/universities/university-of-waterloo/) — University, Waterloo, Canada · 80 works · 15,030 citations
- [Vector Institute](https://md.research.elamur.ai/free/en/labs/vector-institute/) — Research institute, Toronto, Canada · 37 works · 5,703 citations

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## Cite

Cite as: Mariia Ponomarenko, Sepideh Abedini, Masoumeh Shafieinejad, D. B. Emerson, Shubhankar Mohapatra and Xi He. 2026. CAPID: Context-Aware PII Detection for Question-Answering Systems. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ponomarenko2026capid,
  title = {{CAPID: Context-Aware PII Detection for Question-Answering Systems}},
  author = {Mariia Ponomarenko, Sepideh Abedini, Masoumeh Shafieinejad, D. B. Emerson, Shubhankar Mohapatra and Xi He},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-srw.23},
  url = {https://doi.org/10.18653/v1/2026.eacl-srw.23}
}
```

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## References

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*Updated 2 November 2026*