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Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models

21 May 2023
Oana Ignat
Zhijing Jin
Artem Abzaliev
Laura Biester
Santiago Castro
Naihao Deng
Xinyi Gao
Aylin Gunal
Jacky He
Ashkan Kazemi
Muhammad Khalifa
N. Koh
Andrew Lee
Siyang Liu
Do June Min
Shinka Mori
Joan Nwatu
Verónica Pérez-Rosas
Siqi Shen
Zekun Wang
Winston Wu
Rada Mihalcea
    LRM
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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

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