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<p>Large Language Models are transforming communication, research, and decision-making, but misalignment – when models diverge from human values, safety requirements, or user intent – poses serious risks. In this position paper, we argue that many alignment failures stem from operational choices in training and deployment. We posit that alignment should shift from static, post-training constraints toward dynamic, participatory approaches that safeguard pluralism, autonomy, and human flourishing. We outline forward-looking directions, including pluralistic evaluation, transparency, and the Flourishing–Justice–Autonomy (FJA) framework, and present a roadmap for advancing alignment research and practice.</p>

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LLM Alignment should go beyond Harmlessness–Helpfulness and incorporate Human Agency

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月13日

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  • Usman Naseem,
  • Tanmoy Chakraborty,
  • Kai-Wei Chang,
  • Mark Dras,
  • Preslav Nakov,
  • Nanyun Peng,
  • Soujanya Poria

摘要

Large Language Models are transforming communication, research, and decision-making, but misalignment – when models diverge from human values, safety requirements, or user intent – poses serious risks. In this position paper, we argue that many alignment failures stem from operational choices in training and deployment. We posit that alignment should shift from static, post-training constraints toward dynamic, participatory approaches that safeguard pluralism, autonomy, and human flourishing. We outline forward-looking directions, including pluralistic evaluation, transparency, and the Flourishing–Justice–Autonomy (FJA) framework, and present a roadmap for advancing alignment research and practice.