arXiv (All)
open access
When Can First-Order Models of Fine-Tuning Bound Forgetting?
Fine-tuning a language model on new data can make it forget facts that it should keep. We ask whether measurements taken at the start of a fine-tuning run can bound, for each protected fact, the probability that the run makes the model forget it. In LoRA fine-tuning with stochastic gradient descent
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- On the Recoverability of Private Information Unlearning in Large Language Models#799191
- On the Recoverability of Private Information Unlearning in Large Language Models#797768
- Scope Before You Persist: Preventing Cross-Family Interference in Agent Memory#1077073
- The Effect of Forgetting on the Performance of a Synchronizer#629684
- Sleeping Secrets: How Fine-Tuning Reawakens Privacy Risks in Language Models#1160944
Record · ID 1117149
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive