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Knowledge Sanitization of Large Language Models

Paper · 2023 · Yoichi Ishibashi and Hidetoshi Shimodaira

Summary

Large language models trained on web data can memorize private or confidential details and reveal them through extraction attacks or jailbreak prompts. Earlier unlearning methods suppress such facts, but the model may then produce a fabricated substitute, which is a hallucination. Ishibashi and Shimodaira propose "knowledge sanitization," which instead teaches the model to give a fixed safe reply, such as "I don't know," whenever a protected fact is requested. They fine-tune only the MLP weight matrices with LoRA, since prior work suggests factual knowledge is stored mainly in those layers. Training mixes questions whose answers are relabeled with the safe phrase and ordinary question–answer pairs that the model should keep answering correctly. Experiments use LLaMA and GPT-J on closed-book TriviaQA. The sanitized models refuse on the targeted facts while largely keeping their performance on other knowledge and on broader benchmarks. They also resist sentence-completion extraction attacks better than the original model and a ROME-edited model. The approach works on already-trained LLMs without retraining from scratch, offering a lightweight privacy safeguard.

Key points

  • Method: LoRA adapters are applied only to MLP layers. The training set combines a sanitized set K_S, in which answers to forget are replaced with "I don't know," and a retention set K_R, so the model learns when refusal is appropriate.
  • Data: TriviaQA (about 95K QA pairs) is used in closed-book, zero-shot mode. Five answers are chosen at random as forgetting targets (e.g., "Rudyard Kipling"), with 16 training questions each, giving 80 pairs in K_F.
  • Models: LLaMA and GPT-J are evaluated on forgetting and retention, as well as on commonsense reasoning and reading-comprehension tasks.
  • Robustness test: GPT-4-written prompts try to elicit Julius Caesar and related facts (Calpurnia, Cleopatra). The sanitized LLaMA withholds these, while unrelated facts such as Pompeii and Agrippina are still answered. ROME and the original model leak more.
  • Resource: Code and data are published at github.com/yoichi1484/knowledge-sanitization.

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

 Institutions

🎓Kyoto UniversityKyoto · Japan40 works · 8,235 citations🔬RIKEN Center for Advanced Intelligence ProjectTokyo · Japan15 works · 957 citations🔬RIKENWako · Japan26 works · 526 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Yoichi Ishibashi and Hidetoshi Shimodaira. 2023. Knowledge Sanitization of Large Language Models. arXiv:2309.11852.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2309.11852 · arXiv 2309.11852

@misc{ishibashi2023knowledge,
  title = {{Knowledge Sanitization of Large Language Models}},
  author = {Yoichi Ishibashi and Hidetoshi Shimodaira},
  year = {2023},
  doi = {10.48550/arxiv.2309.11852},
  eprint = {2309.11852},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2309.11852}
}

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