mlsanityData to 7 Oct 2026

Secret-Keeping in Question Answering

arXiv 2303.09067 ↗First posted 16 Mar 2023cs.CLcited by 0

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Abstract (arXiv)

Existing question-answering research focuses on unanswerable questions in the context of always providing an answer when a system candots but what about cases where a system bf should not answer a question. This can either be to protect sensitive users or sensitive information. Many models expose sensitive information under interrogation by an adversarial user. We seek to determine if it is possible to teach a question-answering system to keep a specific fact secret. We design and implement a proof-of-concept architecture and through our evaluation determine that while possible, there are numerous directions for future research to reduce system paranoia (false positives), information leakage (false negatives) and extend the implementation of the work to more complex problems with preserving secrecy in the presence of information aggregation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 16 Mar 2023.

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References · 20

  1. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  2. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  3. 2020Extracting Training Data from Large Language Modelscited by 2,144
  4. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  5. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  6. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 452
  7. 2018QuAC : Question Answering in Contextcited by 379
  8. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 337
  9. 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 309
  10. 2022Measuring Forgetting of Memorized Training Examplescited by 118
  11. 2021Truthful AI: Developing and governing AI that does not liecited by 109
  12. 2022Truth Serum: Poisoning Machine Learning Models to Reveal Their Secretscited by 94
8 more
  1. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  2. 2019Fine-Grained Spoiler Detection from Large-Scale Review Corporacited by 76
  3. 2019Overlearning Reveals Sensitive Attributescited by 74
  4. 2020A Survey on Machine Reading Comprehension Systemscited by 41
  5. 2018ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusterscited by 29
  6. 2021No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymizationcited by 13
  7. 2021"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanismcited by 5
  8. 2023Playing to Learn, or to Keep Secret: Alternating-Time Logic Meets Information Theorycited by 1

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