arXiv (All)
open access
Are In-Context Images Worth 10 Dimensions?
There has been significant work on understanding the In-Context Learning capabilities of Large Language Models, especially on the induction circuit. For a few-shot classification task, the induction circuit leverages linear representations of each labeled example in-context in order to classify an u
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
- Large Language Models Develop Belief State Geometry In-Context#1123741
- Large Language Models Develop Belief State Geometry In-Context#919385
- Semantic bias in image-text matching in humans versus vision-language pretraining AI models#1089918
- On the Expressive Power of Transformers for Contextual Relations#1105967
- The First ChineseBabyLM Challenge: training data-efficient and cognitively plausible language models for Chinese#620371
Record · ID 1141972
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