all AI news
DSPy on ICL RAG Classification: Code explained
Feb. 2, 2024, 1 p.m. | code_your_own_AI
code_your_own_AI www.youtube.com
These ICL-RAG examples show that the future of prompt and pipeline engineering. Modular DSPy programs, once optimized, can serve as highly effective general-purpose solutions.
Arxiv preprint (all rights with authors):
In-Context Learning for Extreme Multi-Label Classification
https://arxiv.org/pdf/2401.12178.pdf
Github repo:
https://github.com/KarelDO/xmc.dspy
#newtechnology
#airesearch
art arxiv benchmarks classification code context dspy engineering examples explained future general in-context learning modular modules pipeline prompt rag serve show solutions state
More from www.youtube.com / code_your_own_AI
From Dating Apps to AI: Gen Z Edition 😆
2 days, 17 hours ago |
www.youtube.com
Do not use Llama-3 70B for these tasks ...
3 days, 15 hours ago |
www.youtube.com
New xLSTM explained: Better than Transformer LLMs?
5 days, 17 hours ago |
www.youtube.com
Understand DSPy: Programming AI Pipelines
1 week, 2 days ago |
www.youtube.com
Latest Insights in AI Performance Models
1 week, 4 days ago |
www.youtube.com
New Discovery: Retrieval Heads for Long Context
1 week, 6 days ago |
www.youtube.com
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US