This week’s paper explores EvalGen, a mixed-initative approach to aligning LLM-generated evaluation functions with human preferences. EvalGen assists users in developing both criteria acceptable LLM outputs and developing functions to check these standards, ensuring evaluations reflect the users’ own grading standards.
Read it on the blog: https://arize.com/blog/breaking-down-evalgen-who-validates-the-validators/
To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.
Keys To Understanding ReAct: Synergizing Reasoning and Acting in Language Models
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