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aazizisoufiane llm-auto-labeler - GitHub LLM-Auto-Labeler is an automated text labeling tool designed to reduce the need for time-consuming human intervention It leverages multiple Language Model Mediators (LLMs) to assess, label, and provide justifications for text data
Open Office Hours – Hamel’s Blog These are notes from open office hours, where I answer questions about LLMs It’s open to everyone
Observability in LLM Applications – Hamel’s Blog In this office hours session, I had an insightful conversation with Sebastian Sosa, an engineer grappling with observability challenges in complex LLM systems Our discussion centered around testing and monitoring strategies for applications with multiple points of failure
Tame Complexity By Scoping LLM Evals – Hamel’s Blog Rather than trying to perfect evaluation across all 40 topics, we discussed several approaches: 1 Focus on High-Traffic Topics Instead of trying to excel at everything, focus evaluation efforts on the 5-6 topics that drive most conversations
Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop We will highlight the importance of respectful and responsible use of human annotators, ensuring fair compensation, informed consent, clear instructions, and reasonable working hours to avoid exploitation in any form
What you need to know about Data Labeling and LLMs Training In this article, we’ll explore the mutual-beneficial relationship between data labeling and large language models Data labeling, or data annotation, is a process of identifying, describing, and classifying specific elements in data to train machine learning models