mlsanityData to 7 Oct 2026

What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models

arXiv 2407.01929 ↗First posted 2 Jul 2024cs.CL cs.AIcited by 2

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Abstract (arXiv)

The term Language Models (LMs) as a time-specific collection of models of interest is constantly reinvented, with its referents updated much like the textitShip of Theseus replaces its parts but remains the same ship in essence. In this paper, we investigate this textitShip of Language Models problem, wherein scientific evolution takes the form of continuous, implicit retrofits of key existing terms. We seek to initiate a novel perspective of scientific progress, in addition to the more well-studied emergence of new terms. To this end, we construct the data infrastructure based on recent NLP publications. Then, we perform a series of text-based analyses toward a detailed, quantitative understanding of the use of Language Models as a term of art. Our work highlights how systems and theories influence each other in scientific discourse, and we call for attention to the transformation of this Ship that we all are contributing to.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 38 references that are in the map. First posted on arXiv 2 Jul 2024.

Builds on

16 of 38 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  4. 2022A Survey on In-context Learningcited by 717
  5. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  6. 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
  7. 2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviewscited by 160
  8. 2020Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databasescited by 75
  9. 2023A Bibliometric Review of Large Language Models Research from 2017 to 2023cited by 56
  10. 2019Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Constructioncited by 50
  11. 2022What Do NLP Researchers Believe? Results of the NLP Community Metasurveycited by 28
  12. 2023The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Researchcited by 27
4 more
  1. 2020Examining Citations of Natural Language Processing Literaturecited by 20
  2. 2023We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fieldscited by 20
  3. 2023A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?cited by 14
  4. 2014Inheritance patterns in citation networks reveal scientific memescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.