Robust LLM pipelines (Mathematica, Python, Raku)

preview_player
Показать описание
The presentation discusses techniques for harnessing LLMs in a more robust and effective manner.
(Because LLMs are unreliable and slow.)

-------------

  1. Anton Antonov
  2. LLM
  3. OpenAI
  4. DSL
  5. UML
  6. LLM prompts
Рекомендации по теме
Robust LLM pipelines 1:12:59

Robust LLM pipelines (Mathematica, Python, Raku)

Quantile Regression Workflows 0:43:27

Quantile Regression Workflows (PyData Global 2024)

[GRPO Explained] DeepSeekMath: 1:09:00

[GRPO Explained] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Gödel's Theorem to 0:35:11

Gödel's Theorem to Gödel AI: The Blueprint for Self-Learning Machines

Prolog kullanarak LLM'lerin 0:18:28

Prolog kullanarak LLM'lerin problem çözme kabiliyetlerini arttırmak

Moritz Münchmeyer (Wisconsin-Madison, 0:42:38

Moritz Münchmeyer (Wisconsin-Madison, US): Explorations between Theory and AI in Cosmology

LLM-1 Project Bootcamp: 1:00:19

LLM-1 Project Bootcamp: BeiT & DETR