arXiv (All)
open access
When Context Misleads: In-context Learning with Jurisdiction in Large Language Models
In-Context Learning (ICL) has become a cornerstone of modern LLM deployment. However, existing ICL post-training methods have a critical blind spot: they excel at extracting patterns from demonstrations while often neglecting context authority, the ability to determine whether contextual information
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
- Gender Bias in Vision-Language In-Context Learning#1064226
- Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting#1142986
- LLMs Learn Better In-Context from Rules than from Examples#925571
- Probing Maximally Divergent Conceptual Regions in Large Language Models Through Prompt Engineering#349461
- MCD: Causal Distillation of Multimodal In-Context Learning in Large Vision-Language Models#1145087
Record · ID 1064147
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