@inproceedings{ignat-etal-2024-solved,
    title = "Has It All Been Solved? Open {NLP} Research Questions Not Solved by Large Language Models",
    author = "Ignat, Oana  and
      Jin, Zhijing  and
      Abzaliev, Artem  and
      Biester, Laura  and
      Castro, Santiago  and
      Deng, Naihao  and
      Gao, Xinyi  and
      Gunal, Aylin Ece  and
      He, Jacky  and
      Kazemi, Ashkan  and
      Khalifa, Muhammad  and
      Koh, Namho  and
      Lee, Andrew  and
      Liu, Siyang  and
      Min, Do June  and
      Mori, Shinka  and
      Nwatu, Joan C.  and
      Perez-Rosas, Veronica  and
      Shen, Siqi  and
      Wang, Zekun  and
      Wu, Winston  and
      Mihalcea, Rada",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.708",
    pages = "8050--8094",
    abstract = "Recent progress in large language models (LLMs) has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse that {``}it{'}s all been solved.{''} Not surprisingly, this has, in turn, made many NLP researchers {--} especially those at the beginning of their careers {--} worry about what NLP research area they should focus on. Has it all been solved, or what remaining questions can we work on regardless of LLMs? To address this question, this paper compiles NLP research directions rich for exploration. We identify fourteen different research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. While we identify many research areas, many others exist; we do not cover areas currently addressed by LLMs, but where LLMs lag behind in performance or those focused on LLM development. We welcome suggestions for other research directions to include: https://bit.ly/nlp-era-llm.",
}

Recent progress in large language models (LLMs) has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse that {``}it{'}s all been solved.{''} Not surprisingly, this has, in turn, made many NLP researchers {--} especially those at the beginning of their careers {--} worry about what NLP research area they should focus on. Has it all been solved, or what remaining questions can we work on regardless of LLMs? To address this question, this paper compiles NLP research directions rich for exploration. We identify fourteen different research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. While we identify many research areas, many others exist; we do not cover areas currently addressed by LLMs, but where LLMs lag behind in performance or those focused on LLM development. We welcome suggestions for other research directions to include: https://bit.ly/nlp-era-llm.
Ignat, Oana Jin, Zhijing Abzaliev, Artem Biester, Laura Castro, Santiago Deng, Naihao Gao, Xinyi Gunal, Aylin Ece He, Jacky Kazemi, Ashkan Khalifa, Muhammad Koh, Namho Lee, Andrew Liu, Siyang Min, Do June Mori, Shinka Nwatu, Joan C. Perez-Rosas, Veronica Shen, Siqi Wang, Zekun Wu, Winston Mihalcea, Rada
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models