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This chapter provides a forward-looking analysis of the evolving landscape of interactive natural language processing (iNLP). It discusses key challenges and potential research areas, including improving factual and value alignment in language models, enhancing the realism and complexity of socially embodied agents, and advancing continual learning for model adaptability. The chapter also emphasizes the need for innovations in speed and efficiency, managing context length, generating long coherent texts, and increasing model accessibility. Furthermore, it underscores the importance of developing creative language models and designing comprehensive evaluation frameworks to assess model interactivity. These insights collectively sketch a roadmap for future iNLP advancements.

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Future Directions

  • 章节

  • 发表日期：

    2026年

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  • Zekun Wang,
  • Wenhu Chen,
  • Ruibo Liu,
  • Kexin Yang,
  • Wangchunshu Zhou,
  • Chenghua Lin,
  • Qi Liu,
  • Mong Yuan Sim,
  • Ge Zhang,
  • Xiuying Chen,
  • Ke Xu,
  • Jie Fu

摘要

This chapter provides a forward-looking analysis of the evolving landscape of interactive natural language processing (iNLP). It discusses key challenges and potential research areas, including improving factual and value alignment in language models, enhancing the realism and complexity of socially embodied agents, and advancing continual learning for model adaptability. The chapter also emphasizes the need for innovations in speed and efficiency, managing context length, generating long coherent texts, and increasing model accessibility. Furthermore, it underscores the importance of developing creative language models and designing comprehensive evaluation frameworks to assess model interactivity. These insights collectively sketch a roadmap for future iNLP advancements.