# Reflections on the Use of Generative AI for Research Professions

Soraya Arias, Michel Bergmann, Fabien Campillo, Marie-Agnès Enard, Christian Fabre, Frédérick Garcia, Benjamin Guedj, Emmanuel Jeannot, Giovanni Neglia, Diane Peurichard, et al.

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Soraya Arias, Michel Bergmann, Fabien Campillo, Marie-Agnès Enard, Christian Fabre, et al. Reflections on the Use of Generative AI for Research Professions. Inria. 2025. hal-05188001v2

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# Reflections on the Use of Generative AI for Research Professions.

Prepared by the working group (WG) composed of members of the Evaluation Committee (EC) as follows : Soraya Arias, Michel Bergmann, Fabien Campillo, Marie-Agnès Enard, Christian Fabre, Frédérick Garcia, Benjamin Guedj, Emmanuel Jeannot (coordinator), Giovanni Neglia, Diane Peurichard, Daniel Racoceanu, Benoît Sagot (coordinator) and Gaëlle Tworkowski

Document approved by the Inria Evaluation Committee on July 09 2025

## Contents

<table><tr><td>1 Introduction</td><td>2</td></tr><tr><td>2 Issues and Scope of the WG</td><td>2</td></tr><tr><td>3 Uses and Awareness of Uses</td><td>3</td></tr><tr><td>4 Generative AI as an Assistant for our Professions</td><td>3</td></tr><tr><td>4.1 Writing Articles</td><td>4</td></tr><tr><td>4.2 Research Evaluation</td><td>4</td></tr><tr><td>4.3 Responding to Requests for Proposal</td><td>5</td></tr><tr><td>4.4 Other Uses</td><td>5</td></tr><tr><td>4.5 Declaration of Uses</td><td>6</td></tr><tr><td>5 On the Detection of AI-Generated Texts</td><td>6</td></tr><tr><td>6 Towards a Local/Inria/Sovereign LLM?</td><td>6</td></tr><tr><td>7 Interaction with Students and Training on Generative AI Use</td><td>7</td></tr><tr><td>7.1 Within Teams</td><td>7</td></tr><tr><td>7.2 Teaching</td><td>8</td></tr><tr><td>8 Using Generative AI for Programming</td><td>8</td></tr><tr><td>9 Ethical Questions</td><td>9</td></tr><tr><td>10 Conclusion</td><td>9</td></tr></table>
# 1 Introduction

Since November 2022, and the public release of OpenAI's ChatGPT, the question of the impact of such tools on a wide range of professions has continuously been raised. ChatGPT and generative AI are disrupting our relationship with language, particularly in writing, generating hopes (for example, productivity gains) and fears (for example, loss of skills). The research professions have a close relationship with writing: researchers (but also administrative staff) spend a large part of their time reading documents (articles, reports, files, emails), writing them, and sometimes evaluating them. For these activities, the contribution of ChatGPT can appear as a potential gain both quantitatively and qualitatively. Moreover, in a world where the dominant language is English, ChatGPT helps simplify the dissemination of documents through automatic translation. Lastly, generative AI is not limited to writing and also extends to code generation and image generation, which are also central in research professions in computer science. Thus, Inria is not spared by these questions and disruptions: some staff members are already using generative AI in daily tasks, raising ethical, technical, and practical questions. The Evaluation Committee, with its expertise in evaluation and its unique coverage of Inria's themes and professions, plays a key role in proposing a set of reflections, guidelines, and recommendations on the **responsible use** of generative AI in research professions [1].

# 2 Issues and Scope of the WG

This working group is interested in generative AI tools, defined as tools that, once trained, can generate data from a text or command line (*prompt*). The majority of them use AI architectures based on or inspired by the “*Transformers*” proposed by Google in 2017. These architectures and models differ from language processing tools like translators (Google Translate, Deepl) or grammar checkers (Grammarly, Word) due to their ability, after training a large language model (aka LLM), to automatically generate text and other data based on a semantic context, using a conversational agent (*chatbot*). More specifically, it particularly covers several forms:

* Tools for generating text (like ChatGPT or BERT),

* Tools for generating images (like Dall-E, Midjourney, or Stable Diffusion),

* Tools for generating code (like Codex or Copilot),

* But also tools for generating other modalities (audio *e.g.*, ChatGPT audio version or video *e.g.*, Sora).

These tools are already being adopted by many people in the research community. Indeed, access to these tools has become widely democratized in recent years and is now very simple [2]. It is already unrealistic to want to prohibit their use. However, since their use may raise various issues, this WG aims to offer a framework for reflection on the use of generative AI. The questions and responses provided by this document are therefore multiple.

* How to support the democratization of such tools?

* What uses are possible, and among those, which are acceptable?

* What guidelines and advice should be given for these uses?
* What responsibilities do users have?

* How can the limitations and biases of these tools be understood?

This document provides food for thought and answers to these questions by approaching them from different point of views related from both research teams and administrative service sides.

# 3 Uses and Awareness of Uses

Conversational agents, due to their interface and architecture, allow for highly varied uses.

* They are excellent tools for grammar and spelling correction. Their contextual understanding of text often allows them to surpass traditional tools (e.g., MS-Word's spell checkers, Grammarly).

* The architectures of LLMs easily allow the generation of text in different languages, and especially to correct, summarize, or translate texts in a language not fully mastered by the user. Here again, the contextual understanding of the text allows such tools to surpass what was possible with classical translators (i.e. ChatGPT allows the translation of program comments without altering the code, or the translation of content written in LATEX without altering the tags).

* Conversational agents can improve the phrasing of a text (to change the style, enhance clarity, better structure the argumentation, etc.).

* Conversational agents are also capable of generating text or code based on questions or extracting information from a text. These text generations are contextualized (e.g. they take into account the various previous inputs and outputs).

At this point, it is important to note several things:

1. Conversational agents (almost) always provide a response. However, the reliability of that response is not guaranteed and depends on many factors. Users must, in one way or another, ensure the quality of the results provided by these tools, and in any case, they remain responsible for how they use them.

2. Despite the efforts of the developers of these tools, responses are not entirely free from biases (gender, political, etc.). Once again, it is the responsibility of the users to validate the results and correct these biases.

3. The privacy policies of conversational agents can vary greatly, and it is not ruled out that the data entered in the *prompt* may be stored for purposes other than generating responses. It is therefore important to ensure that the data entered does not have a confidential or personal nature.

# 4 Generative AI as an Assistant for our Professions

We list here several uses of generative AI for research professions by presenting their contributions, impacts, and related issues.
## 4.1 Writing Articles

One common use is to employ conversational agents as aids for writing articles. This can take several forms: grammar and spelling correction, translation, improving style, writing entire paragraphs from an outline, etc. This topic is well-identified by journal editors and program committee chairs of conferences, and many have implemented declarative policies for these types of uses when submitting scientific papers in the name of transparency in research production (with differing responses on whether reviewers should be informed of such use). It is worth noting that some publishers prohibit the use of such tools in this context. In any case, we recommend that researchers comply with the editorial policies of the journals and conferences to which they submit. We remind researchers that they remain responsible for the use of generative AI and the content they submit. In any case, generative AI cannot be considered an author or co-author of an article, as it does not meet the criterion of responsibility for the content submitted, and the nature of scientific production—such as creativity and the development of critical thinking—are tasks intrinsically human. That being said, since generative AI allows for substantial quantitative and qualitative gains, it is expected that there will be an increase in the number of articles submitted to journals and conferences. This trend is already noticeable in certain fields (e.g., machine learning, HPC) [3], but more refined studies will be needed to confirm whether this increase is (partially or entirely) due to the use of LLMs. In any case, it seems beneficial for the various research communities to collectively consider the subject and the potential impacts this will have on how articles are produced and reviewed.

**Recommendation 1**

Users of generative AI must consider themselves and be held responsible for the use they make of it, and for the content they produce and distribute. They remain the sole authors of the content they produce and distribute, even when generative AI has been involved in its production. In particular, they must ensure that the content they distribute does not constitute plagiarism.

## 4.2 Research Evaluation

Another possible use concerns the evaluation of scientific production (reviewing papers), research projects, or researchers' files (for promotions, grants, or job applications). Evaluation is a process that requires contextualization that goes beyond the submitted text and requires an approach that contrasts different points of view. Moreover, the information submitted in a paper, project, or file often contains sensitive information (e.g., confidential or personal). These are shared with the trust that they will not be disclosed beyond the group of reviewers. This confidentiality is broken if such evaluations are conducted with the help of non-open LLMs, such as ChatGPT. Furthermore, the use of LLMs by early-career researchers could impair the development of their skills to write fair and balanced reviews. Thus, for all these reasons, the EC opposes the substantial use (beyond formatting once the text is written) of such tools in research and researcher evaluations. However, some publishers (e.g., Springer Nature) and submission tools are working on developing tools to assist in the review process (like plagiarism detection tools), but with a semantic approach. This is not in contradiction with the previous recommendation (cf. 4.1) as long as such tools remain assistance (and are not used to make the final decision), and the authors have given their consent. We believe that research communities should remain critically attentive to these developments.
### Recommendation 2

Knowing that evaluation remains a complex task involving conflicting perspectives, the EC recommends banning the use of LLMs during, for example, the evaluation steps for job applications and paper reviews.

### Recommendation 3

In the absence of sovereign LLMs, the EC recommends that everyone be extremely vigilant not to transmit, via conversational agents' prompts, confidential (or personal) data. This recommendation applies to all staff (researchers, administrative personnel).

## 4.3 Responding to Requests for Proposal

Writing project proposals is a task that consumes a lot of time with highly variable success rates. Empirically, there is a bias in favor of native English speakers in terms of success rates. However, a project should first be judged on its relevance, the ideas proposed, and the originality of the approach, not on the quality of the language or writing. Even if the style may sometimes be considered stereotypical, we believe that using LLMs to counter the language bias should be seriously considered in particular to easy translation from non English native language or improve the linguistic quality of a text written in English [4]. Indeed, just as in article writing, its use could make such submissions more equitable In this context, reasonable use of LLMs focused on content enhancement, but not on content generation, seems acceptable. Moreover if a sovereign LLM dedicated to the research community is available, confidentiality issues can be overcome.

## 4.4 Other Uses

Among the other possible uses, the WG wants to highlight certain specific cases:

* Writing emails and letters. In our environment, administrative or research personnel spend a significant portion of their time writing and reading emails. In this context, generative AI can be a useful tool for formatting and correcting emails. There are already integrated tools in email clients (like Copilot for Outlook), and we can expect these to become more common<fnref n="1" />.

* Writing reports or minutes. The academic sector, like other administrations, is fond of written output. Given the prevalence of writing in research domains, scientists and administrative staff of the institute spend a significant amount of their time writing documents, notes, minutes, arguments, and other reports. Writing is sometimes seen as tedious, complex, and time-consuming for a result or benefit that can sometimes be low. Thus, in some cases, the use of LLMs should provide valuable assistance and improve productivity. A reflection within the various research communities and professions will need to be conducted on this subject.

* Generative AI, due to its ability to answer questions on sometimes very specialized topics, can be a useful tool to explore a new subject. In some cases, it can be a valuable resource for pointing to classic results in a field that one is just beginning to explore. But we call for extreme cautions on any claim generated by these AI tools when it comes to more recent

<footnote n="1">¹It is worth noting that such uses can usefully benefit from the politeness biases of generative AIs...</footnote>
or more confidential results. These tools are prone to "hallucinations", i.e. they produce
contents that are statistically plausible but factually inaccurate.

* Generative AI is also capable of analyzing data to refine it and begin analyzing it. Here, in accordance with **Recommendation 3** (cf. 4.2), we call for great caution regarding the data transmitted during such analyses: there is no guarantee that the confidentiality of the data is preserved.

* Other modalities. Today, many tools can generate modalities other than text (images, videos, speech). Although these uses raise similar issues to those related to text generation in a professional context, there are some differences: for example, using tools that generate images can help obtain illustrations without infringing copyright laws, as long as the models are not been trained with images whose use for this purpose was not authorized.

## 4.5 Declaration of Uses

The transparency of the use of generative AI is key to maintaining trust between text authors and their readers. Given the variety of uses, there cannot be a binary way of declaring them. A broad reflection on this subject must be carried out at the institute level.

**Recommendation 4**

For transparency, the use of generative AI for certain tasks (text generation, translation, image generation) must be declared. For this, a specific guide must be developed jointly within Inria (with the ethics committee, the legal department, the DGDS, the EC, etc.), addressing:

* the boundary between what should be declared and what is unnecessary, and

* the way to describe this usage.

# 5 On the Detection of AI-Generated Texts

The question of automatically detecting texts generated by AI relates to the fact that despite its stochastic nature, the style of these tools is very stereotyped (cf. article on the increase in the use of adjectives in ICLR reviews [5], [6]). Detecting texts generated by generative AI is a research subject and could be used to trace and verify the legitimate use of these tools, even though using LLMs for text generation does not itself constitute a copyright violation: "the act of appropriating the content generated by a generative AI does not alone constitute a fraudulent alteration of the truth." [7]. Inria could evaluate the current performance and estimate the future performance of such tools, and then determine whether a dedicated tool should be developed and made available to the community.

## 6 Towards a Local/Inria/Sovereign LLM?

Generative AIs, provided as services operated by private, often non-European entities, bring uncertainties regarding the management and use of data for queries (inference). For these reasons,
several international institutes in digital science have launched projects aimed at developing and implementing sovereign LLMs dedicated to research professions (e.g., AuroraGPT in the USA and its equivalent at Riken in Japan). The goal is to train very large models (in the order of 10¹² parameters) on scientific data to develop research assistants. We believe that Inria should pursue a similar project by partnering with other actors in the academic and research sector. To carry out such a project, many scientific and technical questions must be resolved: What training data corpora? How to ensure the quality of the generated model? Who validates the models and how? Meanwhile, for research or productivity purposes, Inria should implement a policy that facilitates access to such tools via license acquisition, as well as a precise definition of responsible use.

**Recommendation 5**

Following the examples in the USA and Japan, the EC recommends that Inria thinks about joining with academic and research actors (and even European actors and/or industry corporations) to develop a sovereign, open source, generative AI (training and evaluation, cf. work on AuraGPT) dedicated to the research community and usable as an assistant in daily tasks of the research and administrative staff.

**Recommendation 6**

Training an LLM requires data. Inria should engage in a reflection on the training data corpora: legal aspects of making training data available (e.g., HAL, data.gouv.fr), licensing issues, technical challenges, etc.

**Recommendation 7**

The EC recommends facilitating the acquisition of generative AI licenses for teams, whether for research or productivity purposes.

# 7 Interaction with Students and Training on Generative AI Use

## 7.1 Within Teams

Inria and the research world maintain strong links with students, whether in training, internships, or PhD work. The goal of education is not only to train students in the techniques and tools of their future professions but also to develop critical thinking and problem-solving skills. The use of ChatGPT and generative AI raises significant concerns about undermining these foundations. Therefore, it is absolutely essential that research team members and staff engage in thorough, open discussions about the explicit use of these tools in a professional setting. We also believe that accessible training sessions should be offered to all institute staff. These training sessions could be developed by researchers in the relevant fields, engineers from the experimental and development services (SED), and a panel of users.
### Recommendation 8

The use of generative AI requires learning. The EC recommends that training sessions be organized in the centers for staff (particularly for young researchers) to help them master these tools and their legitimate uses, as well as understand the limitations and biases of these tools.

### Recommendation 9

The EC recommends that open discussions be held among staff, at the institute level, within teams and services about the use of these tools, focusing on their uses, advantages, and limitations.

## 7.2 Teaching

Generative AI disrupts both the way of teaching and evaluating knowledge (writing content or code). The concern about skill loss is shared by many colleagues. Many Inria staff participate in teaching in one way or another and are therefore affected. This remains an open question, and Inria should contribute to the debate.

# 8 Using Generative AI for Programming

Since computer code is essentially text, and generative AI tools are trained on code, the use of conversational agents for programming is quite natural. In several articles [8], it is noted that tools like ChatGPT and MS Copilot can be used, for example, for:

* Code generation,

* Code optimization,

* Debugging assistance,

* Code documentation,

* Code review.

This represents a radical shift in practice. However, as with text generation, the quality of the generated code can vary greatly. Therefore, as with text, the user remains responsible for the use and final result.

It should be noted that using ChatGPT has led to a reduction in the use of collaborative sites like StackOverflow (cf. Figure 1 of [9]): For example, ChatGPT or Copilot allows initial problem-solving (for languages that may not be very accessible and for which it is not the core of our work, e.g., tikz), without the need to submit queries on community sites where response times may be long. Generative AI goes far beyond conversational agents and text generation. In the realm of code, it raises the following issues:

* Today, LLMs like ChatGPT or Copilot are trained on datasets (programs) that may be entirely retransmitted in response to a question. In such cases, it is possible that this infringes the license of the code used for training.
* Additionally, using LLMs for code generation may potentially lead to disclosing (to the LLM operator) sensitive algorithms or solutions.

* Finally, the programs stored on certain collaborative platforms (i.e. GitHub) may be used for LLM training without necessarily respecting the software license.

### Recommendation 10

Given the many uncertainties surrounding the use of generative AI in programming, the EC recommends that this use be restricted to non-critical code, and encourages personnel to question the collaborative platforms they use for their developments.

## 9 Ethical Questions

The use of LLMs raises ethical questions (environmental, societal) that should be kept in mind during their use.

First, the training phase is very resource-intensive in terms of computation and energy. It involves several days/weeks of computation on the largest global computers, which is very demanding in terms of hardware and energy. The same applies to the inference phase (querying via the prompt). Even though the energy cost of each query is low, the issue of scalability is at stake [10], [11], [12]. Another aspect concerns the fine-tuning phase of the model (*fine-tuning*), which involves tuning the model to improve its responses or reduce its biases. This work is done by a large number of low-qualified and often poorly paid workers [13], [14].

We believe that these ethical issues (among others) lead to the question (which must be debated within the institute and more broadly) of the responsible use of generative AI.

## 10 Conclusion

The use of generative AI is taking an increasingly important place in the world of research. Generative AI will potentially disrupt the way research is conducted, articles are written, and programming is done. The question of responsible use, respecting privacy and ensuring high-quality research, is crucial.

Faced with the challenges posed by generative AI, we must consider generative AI as tools for holding the pen, but not for thinking for us.

This WG has proposed a number of recommendations for the use of these AIs, emphasizing responsibility and education (particularly for younger staff) to instill a minimum level of understanding among team members and service personnel. We also believe that the issue of a dedicated LLM for scientific research should be championed by Inria in the years to come. The WG calls for absolute vigilance regarding issues of confidentiality and privacy.

Finally, given that this is a new and rapidly evolving field, it seems essential to maintain this working group and establish representatives to continue fostering reflection within the institute and the broader research community.
Recommendation 11

The EC recommends that representatives be appointed on this subject and that the working group be maintained to monitor the impact of generative AI on all professions within the institute, as well as to anticipate and support the changes it brings to our society as a whole.

References

[1] ERA Forum Stakeholders’ document *Living guidelines on the RESPONSIBLE USE OF GENERATIVE AI IN RESEARCH*, second version, April 2025 (link).

[2] Nestor Maslej et al. (2025) *The AI Index 2025 Annual Report*, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, avril 2025 (link).

[3] Weixin Liang et al. (2024) *Mapping the Increasing Use of LLMs in Scientific Papers*, arXiv:2404.01268 (link).

[4] Yafei Liang et al. (2024) *The Impact of Large Language Models on Language Proficiencies in Scientific Publications*, available at SSRN: https://ssrn.com/abstract=4992755 or http://dx.doi.org/10.2139/ssrn.4992755 (link).

[5] Weixin Liang et al. (2024) *Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews*, arXiv:2403.07183v2 (link).

[6] Wissam Antoun et al. (2023) *Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?*, in Actes de CORIA-TALN 2023, pages 14–27, Paris, France.

[7] Information report on the challenges of generative artificial intelligence regarding personal data protection and use of generated content, February 14, 2024, National Assembly No. 2207, page 66

[8] Partha Pratim Ray (2023) *ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope*, Internet of Things and Cyber-Physical Systems, volume 3 (link).

[9] Gordon Burtch et al. (2024) *Generative AI Degrades Online Communities. How large language models are influencing online communities.*, Communications of the ACM, March 2024 Issue, volume 67, numéro 3, pages 40-42 (link)

[10] Alexandra Sasha Luccioni et al. (2023) *Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model*, Journal of Machine Learning Research 24, pages 1-15 (link).

[11] Alexandra Sasha Luccioni et al. (2024) *Power Hungry Processing: Watts Driving the Cost of AI Deployment?*, arXiv:2311.16863 (link).

[12] Aida Usmanova et al. (2024) *Reporting and Analysing the Environmental Impact of Language Models on the Example of Commonsense Question Answering with External Knowledge*, arXiv:2408.01453v1 (link).
[13] Billy Perrigo (2023) *Exclusive: OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic*, TIME, January 18 2023 (link).

[14] Kenya AI (2024) *ChatGPT Used Kenyan Workers for Finetuning AI*, June 13 2024 (link).