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Hum.-Robot Interact."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>The comparatively recent advent of Large Language Models (LLMs) has resulted in a wide array of new capabilities and components relevant to Human\u2013Robot Interaction (HRI) researchers. LLMs are being applied to vision, manipulation, planning, reasoning, learning, and HRI problems, frequently as \u201cScarecrows,\u201d in which LLMs serve as black box modules integrated into robot architectures for the purpose of quickly enabling full-pipeline solutions. However, despite this explosion of applications, general questions remain about the best ways to incorporate LLMs into robot architectures, appropriate safety and guardrail considerations, and, critically, how to report properly on HRI research that involves LLMs. In this article, we explore the question of reporting guidelines for HRI researchers who utilize Scarecrows in robot architectures. We identify five key stakeholder groups in the HRI research process, discuss what information each group needs from HRI researchers, and identify appropriate mechanisms for conveying that information from HRI researchers to stakeholders either directly or indirectly. We contribute a set of suggested guidelines regarding what information should be included when researchers disseminate information about HRI research that uses LLMs.<\/jats:p>","DOI":"10.1145\/3777552","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T09:18:24Z","timestamp":1764235104000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Reporting Guidelines for Large Language Models in Human\u2013Robot Interaction"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1383-8120","authenticated-orcid":false,"given":"Cynthia","family":"Matuszek","sequence":"first","affiliation":[{"name":"Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7921-771X","authenticated-orcid":false,"given":"Tom","family":"Williams","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado School of Mines, Golden, Colorado, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0531-743X","authenticated-orcid":false,"given":"Nicholas","family":"DePalma","sequence":"additional","affiliation":[{"name":"Semio Community, Los Angeles, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5664-4687","authenticated-orcid":false,"given":"Ross","family":"Mead","sequence":"additional","affiliation":[{"name":"Semio AI, Inc., Los Angeles, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1590-1787","authenticated-orcid":false,"given":"Ruchen","family":"Wen","sequence":"additional","affiliation":[{"name":"University of Maryland, Baltimore County, Baltimore, Maryland, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8372-1684","authenticated-orcid":false,"given":"Eike","family":"Schneiders","sequence":"additional","affiliation":[{"name":"University of Southampton, Southampton, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6654-8966","authenticated-orcid":false,"given":"Casey","family":"Kennington","sequence":"additional","affiliation":[{"name":"Computer Science, Boise State University, Boise, Idaho, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9603-8537","authenticated-orcid":false,"given":"Alemitu","family":"Bezabih","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado School of Mines, Golden, Colorado, USA"}]}],"member":"320","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"306","volume-title":"Proceedings of International Conference on Artificial Neural Networks","author":"Allgeuer Philipp","year":"2024","unstructured":"Philipp Allgeuer, Hassan Ali, and Stefan Wermter. 2024. 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