TY - GEN
T1 - Defining a New NLP Playground
AU - Li, Sha
AU - Han, Chi
AU - Yu, Pengfei
AU - Edwards, Carl
AU - Li, Manling
AU - Wang, Xingyao
AU - Fung, Yi R.
AU - Yu, Charles
AU - Tetreault, Joel R.
AU - Hovy, Eduard H.
AU - Ji, Heng
N1 - This work is based upon work supported by U.S. DARPA KAIROS Program No. FA8750-19-2-1004, U.S. DARPA CCU Program No. HR001122C0034, U.S. DARPA ECOLE Program No. HR00112390060, U.S. DARPA ITM FA8650-23-C-7316, U.S. DARPA SemaFor Program No. HR001120C0123 and U.S. DARPA INCAS Program No. HR001121C0165. The opinions, views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
PY - 2023
Y1 - 2023
N2 - The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history. This has resulted in concerns that the field will become homogenized and resource-intensive. The new status quo has put many academic researchers, especially PhD students, at a disadvantage. This paper aims to define a new NLP playground by proposing 20+ PhD-dissertation-worthy research directions, covering theoretical analysis, new and challenging problems, learning paradigms, and interdisciplinary applications.
AB - The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history. This has resulted in concerns that the field will become homogenized and resource-intensive. The new status quo has put many academic researchers, especially PhD students, at a disadvantage. This paper aims to define a new NLP playground by proposing 20+ PhD-dissertation-worthy research directions, covering theoretical analysis, new and challenging problems, learning paradigms, and interdisciplinary applications.
UR - https://www.scopus.com/pages/publications/85182765162
UR - https://www.scopus.com/pages/publications/85182765162#tab=citedBy
U2 - 10.18653/v1/2023.findings-emnlp.799
DO - 10.18653/v1/2023.findings-emnlp.799
M3 - Conference contribution
AN - SCOPUS:85182765162
T3 - Findings of the Association for Computational Linguistics: EMNLP 2023
SP - 11932
EP - 11951
BT - Findings of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 2023 Findings of the Association for Computational Linguistics: EMNLP 2023
Y2 - 6 December 2023 through 10 December 2023
ER -