A PhD Student's Perspective on Research in NLP in the Era of Very Large Language Models
Ignat, Oana, Jin, Zhijing, Abzaliev, Artem, Biester, Laura, Castro, Santiago, Deng, Naihao, Gao, Xinyi, Gunal, Aylin, He, Jacky, Kazemi, Ashkan, Khalifa, Muhammad, Koh, Namho, Lee, Andrew, Liu, Siyang, Min, Do June, Mori, Shinka, Nwatu, Joan, Perez-Rosas, Veronica, Shen, Siqi, Wang, Zekun, Wu, Winston, Mihalcea, Rada
–arXiv.org Artificial Intelligence
Recent progress in large language models 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 career -- wonder about what NLP research area they should focus on. This document is a compilation of NLP research directions that are rich for exploration, reflecting the views of a diverse group of PhD students in an academic research lab. While we identify many research areas, many others exist; we do not cover those areas that are 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
arXiv.org Artificial Intelligence
May-21-2023
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