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Thinking beyond the anthropomorphic paradigm benefits LLM research

Paper · 2026 · Lujain Ibrahim and Myra Cheng

Summary

This position paper examines how the habit of describing LLMs in human terms shapes NLP research, and argues that the field relies on it too heavily. The authors first measure the trend. They adapt AnthroScore, a RoBERTa-based masked-LM metric that compares the probability of human versus non-human pronouns in place of an entity such as "model". They make it a binary, text-level indicator: an abstract counts as anthropomorphic if any sentence scores above zero. Applied to arXiv computer science abstracts and the ACL Anthology, the metric shows a steady rise in human-like framing, which is strongest in LLM papers. The authors then propose a framework that links this terminology to underlying assumptions at five lifecycle stages: training, alignment, evaluation, interpreting model behavior, and user interaction. For each stage, they describe under-explored non-anthropomorphic alternatives. They do not call for dropping anthropomorphism altogether. Instead, they argue for using both lenses, since looking past human analogies can open new research and design directions.

Key points

  • The arXiv analysis covers 158,847 CS papers (Jan 2023 – Dec 2024) that mention a "system", "network" or "model". The share of anthropomorphic abstracts rose from 34% to 40%, and for LLM papers from just over 40% to 48%.
  • In the ACL Anthology (2007–2022), 41,836 of 55,185 abstracts contained a target entity. The anthropomorphic share among them more than doubled, from 5% to 11%, which the authors summarize as about a 150% increase since 2007.
  • The measurement uses roberta-base (125M parameters) and needed under 10 GPU hours on a single GPU.
  • The authors pair each challenged assumption with alternatives. Examples include byte-level tokenization and latent-space reasoning instead of subword and natural-language chain-of-thought, control-theory and interpretability-based steering for alignment, and dynamic evaluations instead of tests designed for humans.
  • They suggest reading behaviors such as deception as context-bound role-play simulations rather than evidence of intent. They also recommend new metaphors, insights from cognitive science and linguistics, and structured interfaces instead of chat-only interaction.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Stanford UniversityStanford · United States434 works · 314,419 citations🎓University of OxfordOxford · United Kingdom104 works · 157,183 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Lujain Ibrahim and Myra Cheng. 2026. Thinking beyond the anthropomorphic paradigm benefits LLM research. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.118

@inproceedings{ibrahim2026thinking,
  title = {{Thinking beyond the anthropomorphic paradigm benefits LLM research}},
  author = {Lujain Ibrahim and Myra Cheng},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.118},
  url = {https://doi.org/10.18653/v1/2026.acl-long.118}
}

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