In plain words: Instead of chasing ever-bigger language models, it lays out more than 20 dissertation-sized research directions for academics, especially PhD students. These cover theory, harder problems, new training styles, and uses in other fields.
Abstract
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.
Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi R. Fung, Charles Yu, Joel R. Tetreault, Eduard H. Hovy, Heng Ji
arXiv:2310.20633 · cs.CL · submitted Oct 31, 2023
abstract · pdf · html · EMNLP Findings 2023 "Theme Track: Large Language Models and the Future of NLP"
