This position paper from a large interdisciplinary team argues that "human-centered" LLM development should be rigorous and integrated at every pipeline stage—data sourcing, training, evaluation, deployment—rather than bolted on during post-training or alignment. The authors synthesize perspectives from NLP, human-computer interaction, and responsible AI to propose a framework for Human-Centered LLMs (HCLLMs), emphasizing user needs, values, and real-world context alongside technical benchmarks. The paper offers stage-by-stage recommendations and closes with a case study on the future of work with LLMs.
Main takeaways:
- Most current "human-centered" efforts happen late (post-training RLHF or red-teaming) rather than being designed in from the start.
- The authors propose integrating human priorities—ethics, preferences, values, accessibility—at every stage: system design, data curation, model training, evaluation, and deployment.
- The framework draws on HCI (user experience, participatory design) and responsible AI (fairness, transparency, safety) alongside NLP technical goals.
- The paper is a roadmap and call to action rather than an empirical study; it offers recommendations and a case study on work contexts.
- Key insight: technical capability alone doesn't guarantee beneficial or safe real-world impact; centering human concerns requires intentional design choices throughout the pipeline.