PONE-D-25-35979

Transforming Scholarly Landscapes: The Influence of Large Language Models on Academic Fields beyond Computer Science

PLOS ONE

Dear Dr. Pramanick,

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PLOS ONE

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This work has been funded by the German

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper presents an interesting and novel approach to tracing the diffusion and application of LLMs—particularly traditional ones like BERT—across 22 academic fields outside the domain of CS. It effectively addresses two core research questions: (1) the breadth of LLM adoption in non-CS disciplines, and (2) their evolving temporal usage patterns. The analysis also includes a qualitative investigation of usage adaptations, enriching the contribution.

I appreciate the paper’s clear and straightforward writing style, which makes it accessible to a broad audience. However, this clarity comes at the cost of under specification in some methodological and scoping decisions. For example:

• Why were only traditional LLMs such as BERT included, while more recent architectures (e.g., instruction-tuned or multimodal models) are excluded, especially considering their growing adoption? Is the selection of LLMs skewed toward older or better-documented models? Are recent instruction-tuned or domain-specific LLMs underrepresented?

• Why were only 22 fields selected, and what criteria guided this selection? While these fields do reflect diverse applications, the limitation needs further justification.

• Is citation count a sufficient and reliable proxy for measuring actual influence or usage of LLMs within a field? How are citation intents (e.g., methodological adoption vs. general reference) distinguished?

• Are all citations given equal weight regardless of field norms or citation behaviors? For instance, fields like medicine or law might have very different citation cultures compared to computer science or linguistics.

• How accurate is the automatic classification of a paper into a given field? Were any steps taken to correct or validate misclassified papers?

• Does the paper account for global or linguistic disparities in LLM adoption across fields?

• The paper uses co-authorship with CS-affiliated individuals as a proxy for interdisciplinary collaboration. However, this assumption is problematic and potentially misleading. Many researchers in fields like engineering or linguistics may hold CS degrees, have dual appointments, or regularly publish in CS venues despite being officially affiliated with non-CS departments. Therefore, co-authorship with a "CS author" does not necessarily indicate interdisciplinary collaboration in practice—it may simply reflect intra-disciplinary work by CS-trained researchers operating in adjacent fields. So, why did the authors rely solely on affiliation-based metrics for measuring interdisciplinarity, and have they considered more robust alternatives? For example, analysis of cross-field publication history, topic modeling, or venue diversity could yield a more accurate picture of true cross-disciplinary exchange. Without this, the conclusions about collaboration patterns may be significantly overstated or skewed.

To improve transparency and rigor, I strongly recommend adding a dedicated section titled “Research Methodology” or “Research Methods” .. This section should explicitly describe how each research question was addressed, including data collection, field classification, and analysis procedures. Additionally, a “Threats to Validity” section would be important for addressing potential biases in citation data, model selection, and field delineation.

The discussion section is thought-provoking and contains valuable insights. However, transforming these insights into a concise “Takeaways” or “Key Messages” subsection would enhance readability and impact for the reader.

I thoroughly enjoyed reading this paper and commend the authors for their efforts. I believe this work has good potential, and with further revisions, it can make a valuable contribution to understanding the interdisciplinary reach of LLMs.

Reviewer #2: Thank you for allowing me to review this manuscript. The manuscript investigates the influence of LLMs through a large-scale citation analysis of approximately 148k non-CS papers citing 106 LLMs. The authors examine citation patterns, usage contexts, and ethical discourse across 22 fields using data from Semantic Scholar. The study addresses a timely and important topic. This manuscript presents a valuable descriptive contribution and provides new insights into the diffusion of LLMs across non-CS fields. However, it requires significant revisions, particularly methodological and statistical reliability. I have multiple concerns about this paper:

1. While the manuscript addresses important questions, its contribution is primarily descriptive. It reads more as a survey of citation trends due to the reliance on standard bibliometric techniques (e.g., citation counts, Gini index). I recommend reframing the manuscript as a descriptive exploratory study or providing empirical depth analysis to reflect the systematic and transformative impact analysis.

2. The methodology is generally suitable for large-scale citation analysis, but it suffers from statistical reliability and reproducibility. Specifically, the analysis is limited to descriptive statistics, with no inferential testing to assess the significance or strength of observed patterns. The task extraction process based on trigrams, as well as the heuristic detection of ethical mentions, are not systematically evaluated in the main text. I recommend incorporating statistical modeling techniques, such as regression or hypothesis testing, validating text-mining heuristics using standard metrics (e.g., precision, recall, F1).

3. The "transformative influence" of LLMs is overstated based on the descriptive nature of the evidence. Citation counts and trigram frequencies do not directly demonstrate transformation, influence, or practical impact in these fields. I recommend rephrasing or qualifying claims about "impact" and "influence" to more accurately reflect the descriptive scope of the study.

4. The manuscript is generally well-written, and the structure is coherent. However, there are inconsistencies in the formatting of results and discussion sections, and some key terms (e.g., "task-agnostic models") are used without definition. I recommend standardizing the Results/Discussion structure across sections and clarifying key concepts upon first use.

5. The manuscript refers in S1 Appendix to a curated list of 106 LLMs derived from the Stanford CRFM Ecosystem Graph. However, it is unclear what criteria were used to include or exclude models. Was there a minimum citation threshold or usage filter? Was the selection random or top-down based on influence?

6. What is the difference between # papers citing LLMs and # citations to LLMs in Table 1? It is confusing.

7. S1 and S2 Appendices are valuable, offering critical context and validation for the study’s data and methods. I suggest considering incorporating key elements of S1 and S2 into the main manuscript.

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Reviewer #1: No

Reviewer #2: No

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Transforming Scholarly Landscapes: The Influence of Large Language Models on Academic Fields beyond Computer Science

PONE-D-25-35979R1

Dear Dr. Pramanick,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Bekalu Tadesse Moges

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: The authors have substantially improved the manuscript in response to prior feedback. The revised version clearly reframes the study as a descriptive and systematic mapping of LLM diffusion across non-CS fields. Methodological transparency has been strengthened through added validation metrics for heuristic processes and the inclusion of detailed Research Methodology and Threats to Validity sections. Overall, the revision demonstrates rigor, clarity, and responsiveness. I am satisfied that all my concerns have been addressed, and I recommend acceptance of the manuscript in its current form.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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