Reflections and New Directions for Human-Centered Large Language Models

2026-05-07

AI Summary

This work addresses the prevailing limitation in large language model (LLM) development, wherein human values are typically incorporated only post-training, lacking systematic integration across the model’s entire lifecycle. To bridge this gap, the paper introduces the Human-Centric Large Language Model (HCLLM) framework, which for the first time deeply integrates natural language processing, human-computer interaction, and responsible AI methodologies throughout all stages—from system design and data collection to training, evaluation, and deployment. The framework harmonizes ethical, economic, and technical objectives, offering developers actionable, principle-based guidance. Its forward-looking applicability and practical utility are demonstrated through a case study situated in future workplace scenarios, thereby advancing LLM development toward a genuinely human-centered paradigm.

Abstract

Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, finance, healthcare, law, and science. With this rise in global influence comes greater urgency to build, evaluate, and deploy these systems in a manner that prioritizes not only technical capabilities but also human priorities. This work presents a framework for developing Human-Centered Large Language Models (HCLLMs), which integrates perspectives from Natural Language Processing (NLP), Human-Computer Interaction (HCI), and responsible AI. Considering the ethics, economics, and technical objectives of language modeling, we argue that model developers need to address human concerns, preferences, values, and goals, not only during a cursory post-training stage, but rather with rigor and care at every stage of the pipeline. This paper offers human-centered insights and recommendations for developers at each stage, from system design to data sourcing, model training, evaluation, and responsible deployment. Then we conclude with a case study, applying these insights to understand the future of work with HCLLMs.

Problem

• Human-Centered AI
• Large Language Models
• Responsible AI
• Human-Computer Interaction
• Ethical AI

Innovation

• Human-Centered AI
• Large Language Models
• Responsible AI
• Human-Computer Interaction
• Ethical NLP