mlsanityData to 14 Oct 2026

A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?

arXiv 2305.12920 ↗First posted 22 May 2023cs.CLcited by 14

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Abstract (arXiv)

Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its continuous advancement. In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of the evolution of research topics within NLP and utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data. Subsequently, we leverage this structure to measure the intensity of these relationships. By conducting extensive experiments on the ACL Anthology corpus, we demonstrate that our framework effectively uncovers evolutionary trends and the underlying causes for a wide range of NLP research topics. Specifically, we show that tasks and methods are primary drivers of research in NLP, with datasets following, while metrics have minimal impact.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 16 of its 49 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

16 of 49 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  3. 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
  4. 2018Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Constructioncited by 322
  5. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
  6. 2021Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Researchcited by 102
  7. 2020Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimatescited by 59
  8. 2019Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Constructioncited by 50
  9. 2018Factors Influencing the Surprising Instability of Word Embeddingscited by 33
  10. 2018The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solverscited by 31
  11. 2021TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metricscited by 30
  12. 2020Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confoundingcited by 25
4 more
  1. 2021End-to-End NLP Knowledge Graph Constructioncited by 21
  2. 2020Examining Citations of Natural Language Processing Literaturecited by 20
  3. 2019The State of NLP Literature: A Diachronic Analysis of the ACL Anthologycited by 8
  4. 2017Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Textscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 3; 2026: 5.

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  1. 2024From Insights to Actions: The Impact of Interpretability and Analysis Research on NLPcited by 20
  2. 2024The Nature of NLP: Analyzing Contributions in NLP Paperscited by 12
  3. 2024Efficient Performance Tracking: Leveraging Large Language Models for Automated Construction of Scientific Leaderboardscited by 10
  4. 2024Awes, Laws, and Flaws From Today's LLM Researchcited by 5
  5. 2024Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Sciencecited by 4
  6. 2025Analyzing 16,193 LLM Papers for Fun and Profitscited by 4
  7. 2025A Position Paper on the Automatic Generation of Machine Learning Leaderboardscited by 3
  8. 2024What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Modelscited by 2
  9. 2025A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in Englishcited by 2
  10. 2026Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Frameworkcited by 2
  11. 2026Quantifying the Knowledge Proximity Between Academic and Industry Research: An Entity and Semantic Perspectivecited by 1
  12. 2026ClaimFlow: Tracing the Evolution of Scientific Claims in NLPcited by 1
2 more
  1. 2026How Does Research Evolve? Tracing Cross-Domain Trajectories in NLP, ML, and CV Through Claim-Grounded Typed Citationscited by 0
  2. 2026The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processingcited by 0