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Beltagy I, Lo K, Cohan A. SciBERT: A pretrained language model for scientific text. arXiv preprint arXiv:1903.10676
Original
2019
Earlier work this paper cites.
Feng Z, Guo D, Tang D, et al. Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155
Original
2020
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Floridi L, Chiriatti M. GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines
2020
Earlier work this paper cites.
Chen M, Tworek J, Jun H, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374
Original
2021
Earlier work this paper cites.
Austin J, Odena A, Nye M, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732
Original
2021
Earlier work this paper cites.
Black S, Biderman S, Hallahan E, et al. Gpt-neox-20b: An open-source autoregressive language model. arXiv preprint arXiv:2204.06745
Original
2022
Earlier work this paper cites.
Taylor R, Kardas M, Cucurull G, et al. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085
Original
2022
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Accessed 14-11-2023. https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science
Heaven WD. Why Meta’s latest large language model survived only three days online. MIT Technology Review; 2022 · 2022
Earlier work this paper cites.
Xu FF, Alon U, Neubig G, Hellendoorn VJ. A Systematic Evaluation of Large Language Models of Code. In: MAPS 2022. ACM. Association for Computing Machinery; 2022; New York, NY, USA: 1–10
2022
Earlier work this paper cites.

Similar

Vaithilingam P, Zhang T, Glassman EL. Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In: CHI EA ’22. ACM. Association for Computing Machinery; 2022; New York, NY, USA
2022
Cited alongside, same era.
Chowdhery A, Narang S, Devlin J, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311
Original
2022
Cited alongside, same era.
Min B, Ross H, Sulem E, et al. Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey. ACM Comput. Surv
2023
Cited alongside, same era.
LE F. How ChatGPT is transforming the postdoc experience. Nature
2023
Cited alongside, same era.
Susarla A, Gopal R, Thatcher J, Sarker S. The Janus Effect of Generative AI: Charting the Path for Responsible Conduct of Scholarly Activities in Information Systems. Information Systems Research

Then

Hong Z, Ajith A, Pauloski J, Duede E, Chard K, Foster I. The Diminishing Returns of Masked Language Models to Science. In: ACL. Association for Computational Linguistics; 2023; Toronto, Canada: 1270–1283
2023
Closest in time.
Chang Y, Wang X, Wang J, et al. A survey on evaluation of large language models. arXiv preprint arXiv:2307.03109
Original
2023
Closest in time.
Lai Y, Li C, Wang Y, et al. DS-1000: A natural and reliable benchmark for data science code generation. In: PMLR. ; 2023: 18319–18345
2023
Closest in time.
Ni A, Yin P, Zhao Y, et al. L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models. arXiv preprint arXiv:2309.17446
Original
2023
Closest in time.
Ouyang S, Zhang JM, Harman M, Wang M. LLM is Like a Box of Chocolates: the Non-determinism of ChatGPT in Code Generation. arXiv preprint arXiv:2308.02828

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2023

LLMs for Science: Usage for Code Generation and Data Analysis

Nejjar, Mohamed, Zacharias, Luca, Stiehle, Fabian et al.

Understand

Large language models (LLMs) have been touted to enable increased productivity in many areas of today's work life.

Scientific research as an area of work is no exception: the potential of LLM-based tools to assist in the daily work of scientists has become a highly discussed topic across disciplines.
However, we are only at the very onset of this subject of study.
It is still unclear how the potential of LLMs will materialise in research practice.

Built on

Beltagy I, Lo K, Cohan A. SciBERT: A pretrained language model for scientific text. arXiv preprint arXiv:1903.10676
Original
2019
Earlier work this paper cites.
Feng Z, Guo D, Tang D, et al. Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155
Original
2020
Earlier work this paper cites.
Floridi L, Chiriatti M. GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines
2020
Earlier work this paper cites.
Chen M, Tworek J, Jun H, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374
Original
2021
Earlier work this paper cites.
Austin J, Odena A, Nye M, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732
Original
2021
Earlier work this paper cites.
Black S, Biderman S, Hallahan E, et al. Gpt-neox-20b: An open-source autoregressive language model. arXiv preprint arXiv:2204.06745
Original
2022
Earlier work this paper cites.
Taylor R, Kardas M, Cucurull G, et al. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085
Original
2022
Earlier work this paper cites.
Accessed 14-11-2023. https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science
Heaven WD. Why Meta’s latest large language model survived only three days online. MIT Technology Review; 2022 · 2022
Earlier work this paper cites.
Xu FF, Alon U, Neubig G, Hellendoorn VJ. A Systematic Evaluation of Large Language Models of Code. In: MAPS 2022. ACM. Association for Computing Machinery; 2022; New York, NY, USA: 1–10
2022
Earlier work this paper cites.

Similar

Vaithilingam P, Zhang T, Glassman EL. Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In: CHI EA ’22. ACM. Association for Computing Machinery; 2022; New York, NY, USA
2022
Cited alongside, same era.
Chowdhery A, Narang S, Devlin J, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311
Original
2022
Cited alongside, same era.
Min B, Ross H, Sulem E, et al. Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey. ACM Comput. Surv
2023
Cited alongside, same era.
LE F. How ChatGPT is transforming the postdoc experience. Nature
2023
Cited alongside, same era.
Susarla A, Gopal R, Thatcher J, Sarker S. The Janus Effect of Generative AI: Charting the Path for Responsible Conduct of Scholarly Activities in Information Systems. Information Systems Research

Then

Hong Z, Ajith A, Pauloski J, Duede E, Chard K, Foster I. The Diminishing Returns of Masked Language Models to Science. In: ACL. Association for Computational Linguistics; 2023; Toronto, Canada: 1270–1283
2023
Closest in time.
Chang Y, Wang X, Wang J, et al. A survey on evaluation of large language models. arXiv preprint arXiv:2307.03109
Original
2023
Closest in time.
Lai Y, Li C, Wang Y, et al. DS-1000: A natural and reliable benchmark for data science code generation. In: PMLR. ; 2023: 18319–18345
2023
Closest in time.
Ni A, Yin P, Zhao Y, et al. L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models. arXiv preprint arXiv:2309.17446
Original
2023
Closest in time.
Ouyang S, Zhang JM, Harman M, Wang M. LLM is Like a Box of Chocolates: the Non-determinism of ChatGPT in Code Generation. arXiv preprint arXiv:2308.02828

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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2023

Cited alongside, same era.

Li H, Moon JT, Purkayastha S, Celi LA, Trivedi H, Gichoya JW. Ethics of large language models in medicine and medical research. The Lancet Digital Health

2023

Cited alongside, same era.

Grossmann I, Feinberg M, Parker DC, Christakis NA, Tetlock PE, Cunningham WA. AI and the transformation of social science research. Science

2023

Cited alongside, same era.

Kasneci E, Seßler K, Küchemann S, et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences

2023

Cited alongside, same era.

Smith AL, Greaves F, Panch T. Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models. PLOS Digital Health

2023

Cited alongside, same era.

Original

2023

Closest in time.

Huang D, Bu Q, Zhang J, Xie X, Chen J, Cui H. Bias Assessment and Mitigation in LLM-based Code Generation. arXiv preprint arXiv:2309.14345

Original

2023

Closest in time.

Peng S, Kalliamvakou E, Cihon P, Demirer M. The impact of ai on developer productivity: Evidence from github copilot. arXiv preprint arXiv:2302.06590

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2023

Closest in time.

Accessed 14-11-2023. https://www.fraunhofer.de/en/press/research-news/2023/august-2023/fhgenie-the-fraunhofer-gesellschaft-launches-an-internal-ai-chatbot.html

FhGenie: The Fraunhofer-Gesellschaft launches an internal AI chatbot. Fraunhofer Gesellschaft Press Release; 2023 · 2023

Closest in time.