Papers by David A. Smith
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training (2025.findings-acl)
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Jaydeep Borkar, Matthew Jagielski, Katherine Lee, Niloofar Mireshghallah, David A. Smith, Christopher A. Choquette-Choo
| Challenge: | PII is a sensitive information that can be removed from large-language model training due to evolving curation techniques, or because it was recently scraped for retraining. |
| Approach: | They characterize a phenomenon where PII that appeared earlier in training becomes extractable at a later step after fine-tuning on other PI I. |
| Outcome: | The authors show that PII memorization is a dynamic property of a model that evolves throughout training pipelines and depends on commonly altered design choices. |
Recovering Lexically and Semantically Reused Texts (2021.starsem-1)
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| Challenge: | Writers often repurpose material from existing texts when composing new documents. |
| Approach: | They propose to use local text reuse detection to detect localized regions of lexically or semantically similar text embedded in otherwise unrelated material. |
| Outcome: | The proposed methods perform better on three LTRD tasks, detecting plagiarism, modeling journalists’ use of press releases, and identifying scientists’ citation of earlier papers. |
Through the Lens of History: Methods for Analyzing Temporal Variation in Content and Framing of State-run Chinese Newspapers (2025.naacl-long)
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| Challenge: | State-run newspapers are believed to strategically select and frame news articles to align with the shifting political tides of the country. |
| Approach: | They analyze more than 50 years of articles from the People's Daily and Reference News to quantify differences in content and framing over time. |
| Outcome: | The proposed methods show that the changes in name mentions and sentiment in news articles are more significant in People’s Daily than in Reference News . |