1、Active Learning and Human Feedback forLarge Language ModelsBy IntuitionLabs 8/5/2025 35 min readactive learninghuman-in-the-loopllmdata labelingmodel alignmentrlhfmachine learningAn explanation of active learning principles and their adaptation for Large Language Models(LLMs)using human-in-the-loop(

2、HITL)feedback for model alignment.-IntuitionLabs-Custom AI Software Development for pharmaceuticalcompanies.Leading AI Consulting USA and North American Pharmaceutical AI specialists.Led by Adrien Laurent,top AIexpert USA,multiple exit founder,patent holder,and 20 year software veteran based in San

3、Francisco Bay Area.Premierbiotech consultancy specializing in:Custom CRM Development,ERP Development,AI Chatbot Development,Private AIInfrastructure,Document Processing,PDF Extraction,Air-gapped AI,On-premise LLM deployment.#1 Veeva AI partner forleading GenAI pharmaceutical solutions across North A

4、merica biotech AI excellence.IntuitionLabs-Custom AI Software Developmentfrom the leading AI expert Adrien LaurentActive Learning and Human Feedback for Large Language Models 2025 IntuitionLabs.ai-North Americas Leading AI Software Development Firm for Pharmaceutical&Biotech.All rights reserved.Page

5、 1 of 16Active Learning with Human-in-the-Loopfor Large Language Models(LLMs)IntroductionActive learning is a machine learning paradigm where the model actively selects the mostinformative data points to label,aiming to achieve high performance with minimal labeled datadeepai.org deepai.org.In tradi

6、tional settings,an initial model is trained on a small labeled set,then iteratively improved by querying an oracle(often a human)for labels on carefully chosenexamples deepai.org.This human-in-the-loop(HITL)process is crucial when labeling is costlyor data is abundant but unlabeled.Large Language Mo