Defining a New NLP Playground

• URL: http://arxiv.org/abs/2310.20633v1
• Date: Tue, 31 Oct 2023 17:02:33 GMT
• Title: Defining a New NLP Playground
• Authors: 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
• Abstract summary: The recent explosion of performance of large language models has changed the field of Natural Language Processing more abruptly and seismically than any other shift in the field's 80-year history. This paper proposes 20+ PhD-dissertation-worthy research directions, covering theoretical analysis, new and challenging problems, learning paradigms, and interdisciplinary applications.
• Score: 85.41973504055588
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• The Nature of NLP: Analyzing Contributions in NLP Papers [77.31665252336157]
  We quantitatively investigate what constitutes NLP research by examining research papers. Our findings reveal a rising involvement of machine learning in NLP since the early nineties. In post-2020, there has been a resurgence of focus on language and people.
  arXiv  Detail & Related papers  (2024-09-29T01:29:28Z)
• Position: Topological Deep Learning is the New Frontier for Relational Learning [51.05869778335334]
  Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning.
  arXiv  Detail & Related papers  (2024-02-14T00:35:10Z)
• Large Language Model for Science: A Study on P vs. NP [88.67249044141529]
  We use large language models (LLMs) to augment and accelerate research on the P versus NP problem. Specifically, we propose Socratic reasoning, a general framework that promotes in-depth thinking with LLMs for complex problem-solving. Our pilot study on the P vs. NP problem shows that GPT-4 successfully produces a proof schema and engages in rigorous reasoning throughout 97 dialogue turns.
  arXiv  Detail & Related papers  (2023-09-11T17:49:27Z)
• Exploring the Landscape of Natural Language Processing Research [3.3916160303055567]
  Several NLP-related approaches have been surveyed in the research community. A comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.
  arXiv  Detail & Related papers  (2023-07-20T07:33:30Z)
• SciMON: Scientific Inspiration Machines Optimized for Novelty [68.46036589035539]
  We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. We take a dramatic departure with a novel setting in which models use as input background contexts. We present SciMON, a modeling framework that uses retrieval of "inspirations" from past scientific papers.
  arXiv  Detail & Related papers  (2023-05-23T17:12:08Z)
• Searching for chromate replacements using natural language processing and machine learning algorithms [0.0]
  This study demonstrates it is possible to extract knowledge from the automated interpretation of the scientific literature and achieve expert human level insights. We have employed the Word2Vec model, previously explored by others, and the BERT model - applying them towards a unique challenge in materials engineering.
  arXiv  Detail & Related papers  (2022-08-11T07:21:18Z)
• Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey [8.427521246916463]
  Pretrained Language Models (PLM) have established a new paradigm through learning informative representations on large-scale text corpus. This new paradigm has revolutionized the entire field of natural language processing, and set the new state-of-the-art performance for a wide variety of NLP tasks. To address this issue, integrating knowledge into PLMs have recently become a very active research area and a variety of approaches have been developed.
  arXiv  Detail & Related papers  (2021-10-16T03:27:56Z)
• Systematic Inequalities in Language Technology Performance across the World's Languages [94.65681336393425]
  We introduce a framework for estimating the global utility of language technologies. Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies and more linguistic NLP tasks.
  arXiv  Detail & Related papers  (2021-10-13T14:03:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.