arXiv (All)
open access
APOLO: Automatic Prompt Optimization for Ontology Learning
Ontology Learning (OL) from text has advanced with the emergence of Large Language Models (LLMs), but it remains challenging due to the limited availability of annotated training data and the difficulty of adapting LLMs to perform OL effectively. We address this via APOLO - Automatic Prompt Optimiza
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
- MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization#1118430
- Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI#1155617
- WordPolo: Evaluating Language Models Through Iterative Semantic Feedback#1013219
- Knowing When to Critique: Task-Adaptive Metacognitive Regulation for Reliable LLM Reasoning#1118403
- Beyond Prompt or Skill? Attribution-Guided Optimization of Modular LLM Programs#1116823
Record · ID 1128806
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