Declare LLM Usage and Role

Summary: Researchers must disclose any use of LLMs to support empirical studies in their paper, specifying which LLM was used, how it was used, and where in the research process it was employed. They should report the exact purpose, the tasks that were automated, and the expected benefits in the paper. When the LLM is central to the study, the declaration should be prominent and detailed in the methodology section; for tangential uses, a brief statement in the methodology or acknowledgments suffices.

Rationale

Transparency about LLM involvement is a prerequisite for informed assessment of a study’s scope, limitations, and potential biases. Without explicit disclosure, readers cannot evaluate how the LLM’s characteristics may have influenced the research process or its outcomes.

Recommendations

When conducting any kind of empirical study involving LLMs, researchers must clearly declare that an LLM was used (see Scope for what we consider relevant research support). This should be done in a suitable section of the paper, for example, in the introduction or research methods section; Cheng, Calhoun, and Reedy (2025) argue specifically for the methods section, since acknowledgments are easily missed at the end of a paper. For authoring scientific articles, this transparency is, for example, required by the ACM Policy on Authorship: “The use of generative AI tools and technologies to create content is permitted but must be fully disclosed in the Work” (Association for Computing Machinery 2023).

Beyond generic declarations, researchers should report the exact purpose of using an LLM in a study, the tasks it was used to automate, and the expected benefits in the paper. A sufficient declaration specifies not only that an LLM was used, but also which LLM (name and version), how it was used (e.g., as an annotator, code generator, or judge), and where in the research process it was employed (e.g., data collection, analysis, or synthesis).

When the LLM is central to the study (e.g., as the main tool being evaluated or as a core component of the research method), the declaration should be prominent and detailed, appearing in the methodology section with cross-references to the specific guidelines that apply (e.g., Sections Version and Configuration, System and Prompt Design, and Session Traces). When the LLM’s role is more tangential (e.g., used for a single preprocessing step), a brief but explicit statement in the methodology or acknowledgments section is sufficient. When a study assigns multiple distinct roles to LLMs (e.g., one model generates evaluation data while another scores outputs), each role should be declared separately. In each case, the disclosure must be specific enough for readers to assess how the LLM’s involvement may affect the study’s validity and reproducibility.

Examples

The ACM Policy on Authorship (Association for Computing Machinery 2023) suggests disclosing GenAI usage in the acknowledgments section of the paper, advising to “err on the side of caution, and include a disclosure in the acknowledgments section of the Work” when uncertain about the need. For double-blind review, researchers can add a temporary “AI Disclosure” section where the acknowledgments would appear. An example of an LLM disclosure beyond writing support can be found in a recent paper by Lubos et al. (2024), in which they write in the methodology section:

“We conducted an LLM-based evaluation of requirements utilizing the Llama 2 language model with 70 billion parameters, fine-tuned to complete chat responses…”

A more contemporary declaration could similarly state:

“We used Claude Opus 4.7 via the Anthropic API to synthesize themes from interview transcripts, with all prompts and conversation logs published as supplementary material.”

Golnari et al. (2026)’s DevBench paper illustrates separate disclosure of multiple LLM roles: GPT-4o generates the synthetic benchmark instances, nine models (including Claude 4 Sonnet, GPT-4.1, DeepSeek-V3, and Ministral-3B) are the evaluation subjects, and o3-mini is the LLM judge that scores completions for relevance and helpfulness (Golnari et al. 2026).

Benefits

Transparency in the use of LLMs helps other researchers understand the context and scope of the study, supporting interpretation and comparison of the results. Realizing these benefits requires reporting the LLM’s exact role and version (see Version and Configuration).

Challenges

Declaring LLM usage requires only a brief statement and no additional experiments, making compliance straightforward. One challenge might be authors’ reluctance to disclose LLM usage for valid use cases, because they fear that AI-generated content makes reviewers think that the authors’ work is less original. In fact, there is evidence suggesting that AI disclosure can negatively affect trust in authors (Schilke and Reimann 2025). However, the ACM Policy on Authorship is very clear in that any use of GenAI tools to create content must be disclosed. Our guidelines focus on research support beyond proof-reading and writing support (see Scope), but the threshold of what must be declared continues to evolve as organizations such as the ACM update their authorship policies.

Study Types

Researchers must follow this guideline for all study types. The specific focus of the declaration varies by study type. For LLMs as Annotators, LLMs as Judges, LLMs for Synthesis, and LLMs as Subjects, researchers must declare the specific role assigned to the LLM (e.g., annotator, judge, synthesizer, or simulated participant). For Studying LLM Usage, researchers must clarify which LLM(s) the observed participants used and under which conditions. For LLMs for Tools, researchers must declare the LLM’s role within the tool architecture and its contribution to the tool’s functionality. For Benchmarking LLMs, researchers must declare which LLMs were benchmarked and for which tasks.

Advice for Reviewers

The most common problem with disclosure is incompleteness or vagueness about how the LLM was used. If the paper says “we used LLM X to help with task Y” without specifying how, reviewers should request clarification. Such requests are typically minor revisions unless the missing details may reveal methodological problems.

Using an LLM as a copyeditor becomes problematic when authors do not or cannot take responsibility for the resulting text. If a reviewer finds clear evidence that LLM-generated text was not carefully reviewed (e.g., text such as “As a Large Language Model, I…”), this may warrant rejection. If a reviewer suspects an author cannot take responsibility for AI-generated text, they should raise the concern with their editor or program chair. Due process requires that authors not be accused of misconduct without clear evidence.

If undisclosed LLM use is suspected, the reviewer should similarly consult their editor or program chair. When the evidence is conclusive, the key question is the degree to which undisclosed use affects the study’s contribution, ranging from negligible (e.g., word choice in a single sentence) to severe (e.g., generating ostensibly empirical data or statistical analyses).

See Also

• Report Model Version, Configuration, and Customizations: The disclosure is incomplete without naming the specific model and version.
• Report System and Prompt Design: When the LLM lives inside a tool or agent, authors must also describe that tool’s architecture and prompts.
• Report Session Traces: Session traces show what the LLM did during the study.

References

Association for Computing Machinery. 2023. “ACM Policy on Authorship.” https://www.acm.org/publications/policies/new-acm-policy-on-authorship.

Cheng, Adam, Aaron Calhoun, and Gabriel Reedy. 2025. “Artificial Intelligence-Assisted Academic Writing: Recommendations for Ethical Use.” Advances in Simulation 10 (1): 26. https://doi.org/10.1186/s41077-025-00350-6.

Golnari, Pareesa Ameneh, Adarsh Kumarappan, Wen Wen, Xiaoyu Liu, Gabriel Ryan, Yuting Sun, Shengyu Fu, and Elsie Nallipogu. 2026. “DevBench: A Realistic, Developer-Informed Benchmark for Code Generation Models.” CoRR abs/2601.11895. https://doi.org/10.48550/ARXIV.2601.11895.

Lubos, Sebastian, Alexander Felfernig, Thi Ngoc Trang Tran, Damian Garber, Merfat El Mansi, Seda Polat Erdeniz, and Viet-Man Le. 2024. “Leveraging LLMs for the Quality Assurance of Software Requirements.” In 32nd IEEE International Requirements Engineering Conference, RE 2024, Reykjavik, Iceland, June 24-28, 2024, edited by Grischa Liebel, Irit Hadar, and Paola Spoletini, 389–97. IEEE. https://doi.org/10.1109/RE59067.2024.00046.

Schilke, Oliver, and Martin Reimann. 2025. “The Transparency Dilemma: How AI Disclosure Erodes Trust.” Organizational Behavior and Human Decision Processes 188: 104405. https://doi.org/10.1016/j.obhdp.2025.104405.