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        <title>Thinking beyond the anthropomorphic paradigm benefits LLM research</title>
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    <abstract>Anthropomorphism, or the attribution of human traits to technology, is an automatic and unconscious response that occurs even in those with advanced technical expertise. In this position paper, we analyze hundreds of thousands of research articles to present empirical evidence of the prevalence and growth of anthropomorphic terminology in research on large language models (LLMs). We argue for challenging the deeper assumptions reflected in this terminology — which, though often useful, may inadvertently constrain LLM development — and broadening beyond them to open new pathways for understanding and improving LLMs. Specifically, we identify and examine five anthropomorphic assumptions that shape research across the LLM development lifecycle. For each assumption (e.g., that LLMs must use natural language for reasoning, or that they should be evaluated on benchmarks originally meant for humans), we demonstrate empirical, non-anthropomorphic alternatives that remain under-explored yet offer promising directions for LLM research and development.</abstract>
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%0 Conference Proceedings
%T Thinking beyond the anthropomorphic paradigm benefits LLM research
%A Ibrahim, Lujain
%A Cheng, Myra
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ibrahim-cheng-2026-thinking
%X Anthropomorphism, or the attribution of human traits to technology, is an automatic and unconscious response that occurs even in those with advanced technical expertise. In this position paper, we analyze hundreds of thousands of research articles to present empirical evidence of the prevalence and growth of anthropomorphic terminology in research on large language models (LLMs). We argue for challenging the deeper assumptions reflected in this terminology — which, though often useful, may inadvertently constrain LLM development — and broadening beyond them to open new pathways for understanding and improving LLMs. Specifically, we identify and examine five anthropomorphic assumptions that shape research across the LLM development lifecycle. For each assumption (e.g., that LLMs must use natural language for reasoning, or that they should be evaluated on benchmarks originally meant for humans), we demonstrate empirical, non-anthropomorphic alternatives that remain under-explored yet offer promising directions for LLM research and development.
%U https://aclanthology.org/2026.acl-long.118/
%P 2551-2563

Markdown (Informal)

[Thinking beyond the anthropomorphic paradigm benefits LLM research](https://aclanthology.org/2026.acl-long.118/) (Ibrahim & Cheng, ACL 2026)

• Thinking beyond the anthropomorphic paradigm benefits LLM research (Ibrahim & Cheng, ACL 2026)

ACL

• Lujain Ibrahim and Myra Cheng. 2026. Thinking beyond the anthropomorphic paradigm benefits LLM research. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2551–2563, San Diego, California, United States. Association for Computational Linguistics.