arXiv (All)
open access
Persona Learning from Feedback for Personalized AI Assistants
Personalizing AI assistants requires adapting their behavior to users' preferences and task contexts. Existing work elicits assistant personas through persona prompting or finetuning on persona-conditioned generations. However, persona descriptions alone do not identify what to change or preserve in
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
- FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization#1388077
- Show Me Your Prompts! How Writers Feel About Sharing Prompts in Collaborative Text Editors#1083885
- Longitudinal Dataset of Psychological Variables Influencing AI Perception and Usage#178403
- Persona-Guided LLM Agents for Task-Oriented Dialogue#1447244
- Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions#798789
Record · ID 1489631
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