SocArXiv
metadata only
Benchmarking the Personalization Capabilities of Large Language Models
Personalization is classically a two-party problem: a sender chooses what to say, and a receiver with independent objectives decides whether to act. A salesperson pitching the same analytics product leads with HIPAA compliance for a hospital and real-time reporting for a retailer, expecting a differ
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
- Benchmarking the Personalization Capabilities of Large Language Models#1217525
- Learning Personalized Prompts for Healthcare Guidance#798576
- Learning Personalized Prompts for Healthcare Guidance#797163
- Cost-Performance Evaluation of Large Language Models for Aspect-Based Sentiment Analysis of HCAHPS Patient Comments: A Validation Study#361584
- Making shiny objects illuminating: the promise and challenges of large language models in U.S. health systems.#375419
Record · ID 1360697
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