arXiv (All)
open access
Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization
Automatic prompt optimization (APO) uses feedback from model responses to improve prompts automatically. However, its effectiveness is usually measured through overall scores, which do not reveal which questions become correct, which previously correct answers become wrong, or whether improvements e
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
- Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization#1325653
- Answer Set Programming on Expert Feedback to Populate and Extend#119406
- Prompt as a Data Type: In-Database LLM Prompt Management and Rewriting#411179
- Navigating The Feedback Loop: Understanding The Efficacy and Equity Implications of Automated Code Critiquers in Introductory Engineering Programming#210673
- Readings in Optimization#67003
Record · ID 1344907
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