Pressure to publish introduces large-language model risks

被引:0
|
作者
Johnson, Thomas F. [1 ]
Simmons, Benno I. [2 ]
Millard, Joseph [3 ]
Strydom, Tanya [1 ]
Danet, Alain [1 ]
Sweeny, Amy R. [1 ]
Evans, Luke C. [4 ]
机构
[1] Univ Sheffield, Sch Biosci, Ecol & Evolutionary Biol, Sheffield, England
[2] Univ Exeter, Coll Life & Environm Sci, Ctr Ecol & Conservat, Penryn, England
[3] Nat Hist Museum, Biodivers Futures Lab, London, England
[4] Univ Reading, Sch Biol Sci, Ecol & Evolutionary Biol, Reading, England
来源
METHODS IN ECOLOGY AND EVOLUTION | 2024年 / 15卷 / 10期
基金
英国自然环境研究理事会;
关键词
ecology; evolution; large-language models; paper hacking; publish or perish;
D O I
10.1111/2041-210X.14397
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Large-language models (LLMs) have the potential to accelerate research in ecology and evolution, cultivating new insights and innovation. However, whilst revelling in the plethora of opportunities, researchers need to consider that LLM use could also introduce risks. An important piece of context underpinning this perspective is the pressure to publish, where research careers are defined, at least partly, by publication metrics like number of papers, impact factor, citations etc. Coupled with academic employment insecurity, especially during early career, researchers may reason that LLMs are a low-risk and high-reward tool for publication. However, this pressure to publish can introduce risks if LLMs are used as a shortcut to game publication metrics instead of a tool to support true innovation. These risks may ultimately reduce research quality, stifle researcher development and incur reputational damage for researchers and the entire scientific record. We conclude with a series of recommendations to mitigate the magnitude of these risks and encourage researchers to apply caution whilst maximising LLM potential.
引用
收藏
页码:1771 / 1773
页数:3
相关论文
共 50 条
  • [21] What Have Large-Language Models and Generative Al Got to Do With Artificial Life?
    Dorin, Alan
    Stepney, Susan
    ARTIFICIAL LIFE, 2023, 29 (02) : 141 - 145
  • [22] Large-Language Models in Orthodontics: Assessing Reliability and Validity of ChatGPT in Pretreatment Patient Education
    Vassis, Stratos
    Powell, Harriet
    Petersen, Emma
    Barkmann, Asta
    Noeldeke, Beatrice
    Kristensen, Kasper D.
    Stoustrup, Peter
    CUREUS JOURNAL OF MEDICAL SCIENCE, 2024, 16 (08)
  • [23] Review of the opportunities and challenges to accelerate mass-scale application of smart grids with large-language models
    Shi, Heng
    Fang, Lurui
    Chen, Xiaoyang
    Gu, Chenghong
    Ma, Kang
    Zhang, Xinsong
    Zhang, Zhong
    Gu, Juping
    Lim, Eng Gee
    IET SMART GRID, 2024, : 737 - 759
  • [24] Risks and Benefits of Large Language Models for the Environment
    Rillig, Matthias C.
    agerstrand, Marlene
    Bi, Mohan
    Gould, Kenneth A.
    Sauerland, Uli
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, 57 (09) : 3464 - 3466
  • [25] Testing Learning-Enabled Cyber-Physical Systems with Large-Language Models: A Formal Approach
    Zheng, Xi
    Mok, Aloysius K.
    Piskac, Ruzica
    Lee, Yong Jae
    Krishnamachari, Bhaskar
    Zhu, Dakai
    Sokolsky, Oleg
    Lee, Insup
    COMPANION PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, FSE COMPANION 2024, 2024, : 467 - 471
  • [26] Evaluation of Patient Education Materials From Large-Language Artificial Intelligence Models on Carpal Tunnel Release
    Croen, Brett J.
    Abdullah, Mohammed S.
    Berns, Ellis
    Rapaport, Sarah
    Hahn, Alexander K.
    Barrett, Caitlin C.
    Sobel, Andrew D.
    HAND-AMERICAN ASSOCIATION FOR HAND SURGERY, 2024,
  • [27] Risks and Benefits of Large Language Models for the Environment
    Rillig, Matthias C.
    Agerstrand, Marlene
    Bi, Mohan
    Gould, Kenneth A.
    Sauerland, Uli
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, : 3464 - 3466
  • [28] 23 Security Risks in Black-Box Large Language Model Foundation Models
    Mcgraw, Gary
    Bonett, Richie
    Figueroa, Harold
    Mcmahon, Katie
    COMPUTER, 2024, 57 (04) : 160 - 164
  • [29] Instruction-Tuned Large-Language Models for Quality Control in Automatic Item Generation: A Feasibility Study
    Gorgun, Guher
    Bulut, Okan
    EDUCATIONAL MEASUREMENT-ISSUES AND PRACTICE, 2025, 44 (01) : 96 - 107
  • [30] Can Large-Language Models Replace Humans in Agile Effort Estimation? Lessons from a Controlled Experiment
    Pavlic, Luka
    Saklamaeva, Vasilka
    Beranic, Tina
    APPLIED SCIENCES-BASEL, 2024, 14 (24):