arXiv (All)
open access
Capability Self-Assessment in Large Language Models
The ability to recognize one's own limitations and decide whether to solve a problem or seek help is fundamental to reliable intelligent systems. Yet modern large language models tend to overestimate their competence and attempt queries they cannot solve. We study how models can learn Capability 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
- CQ4OE: A benchmark for assessing LLM-assisted ontology generation from competency questions#1061934
- From annotation to reasoning: Culture in language models#1105456
- Post-Training as Distributional Intervention: The Mismatch Between Stated Goals and Actual Operations#209862
- Where Do Large Language Models Fail on Competitive Programming? A Taxonomy of Failures by Algorithm Type and Difficulty Rating#259575
- A FRAMEWORK FOR GUIDING TRAINING DESIGN: ALIGNING TASKS, KNOWLEDGE, SKILLS, AND ABILITIES, AND PERFORMANCE MEASURES TO CUSTOM COMPETENCY MODELS#674652
Record · ID 1116308
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