arXiv (All)
open access
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback
User feedback enables LLMs to refine reusable personalized skills, but often mixes persistent preferences with request-specific corrections. Indiscriminately retaining both risks negative transfer, motivating selective retention at the level of individual requirements. We frame this challenge as sel
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
- MERIT: Matching Expertise via Rubric-Informed Training for Reviewer Assignment#998239
- SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation#793550
- SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation#789546
- Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions#798789
- Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions#797375
Record · ID 1345203
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