This week we explore ReAct, an approach that enhances the reasoning and decision-making capabilities of LLMs by combining step-by-step reasoning with the ability to take actions and gather information from external sources in a unified framework.
To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.
Breaking Down EvalGen: Who Validates the Validators?
Demystifying Chronos: Learning the Language of Time Series
Anthropic Claude 3
Reinforcement Learning in the Era of LLMs
Sora: OpenAI’s Text-to-Video Generation Model
RAG vs Fine-Tuning
Phi-2 Model
HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels
A Deep Dive Into Generative's Newest Models: Gemini vs Mistral (Mixtral-8x7B)–Part I
How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings
The Geometry of Truth: Emergent Linear Structure in LLM Representation of True/False Datasets
Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
Explaining Grokking Through Circuit Efficiency
Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior
Skeleton of Thought: LLMs Can Do Parallel Decoding
Llama 2: Open Foundation and Fine-Tuned Chat Models
Lost in the Middle: How Language Models Use Long Contexts
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Create your
podcast in
minutes
It is Free
The Universe Speaks in Numbers
Breaking Math Podcast
Opinionated History of Mathematics
Biostatistics Podcast
SOA Podcasts - Society of Actuaries