@inproceedings{guo-wu-2025-maji,
    title = "{MAJI}: A Multi-Agent Workflow for Augmenting Journalistic Interviews",
    author = "Guo, Kaiwen  and
      Wu, Yimeng",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.58/",
    pages = "1061--1083",
    ISBN = "979-8-89176-298-5",
    abstract = "Journalistic interviews are creative, dynamic processes where success hinges on insightful, real-time questioning. While Large Language Models (LLMs) can assist, their tendency to generate coherent but uninspired questions optimizes for probable, not insightful, continuations. This paper investigates whether a structured, multi-agent approach can overcome this limitation to act as a more effective creative partner for journalists. We introduce MAJI, a system designed for this purpose, which employs a divergent-convergent architecture: a committee of specialized agents generates a diverse set of questions, and a convergent agent selects the optimal one. We evaluated MAJI against a suite of strong LLM baselines. Our results demonstrate that our multi-agent framework produces questions that are more coherent, elaborate, and original (+36.9{\%} for our best model vs. a standard LLM baseline), exceeded strong LLM baselines on key measures of creative question quality. Most critically, in a blind survey, professional journalists preferred MAJI{'}s selected questions over those from the baseline by a margin of more than two to one. We present the system{'}s evolution, highlighting the architectural trade-offs that enable MAJI to augment, rather than simply automate, journalistic inquiry. We will release the code upon publication."
}