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21 papers in the last four weeks, up 40% on the four weeks before. 0.2% of all new papers.

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Thinking beyond the anthropomorphic paradigm benefits LLM research
Feb 13, 2025Lujain Ibrahim, Myra ChengAnthropomorphism Natural Language
University of Oxford · Stanford University
Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation
Dec 10, 2024Qinhong Lin, Yinglun Feng, Yuhao Zhang +2Large Language Model Uncertainty Uncertainty
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China · QuanCheng Laboratory, Jinan, China
An Incomplete Loop: Deductive, Inductive, and Abductive Reasoning in Language Models
Apr 3, 2024Emmy Liu, Graham Neubig, Jacob AndreasLLM Reasoning Strategies Deduction
Language Technologies Institute Carnegie Mellon University · CSAIL Massachusetts Institute of Technology
GitSkills: A Dataset of Agent Skills on GitHub
Date pendingGiuseppe Destefanis, Daniel Graziotin, Matteo Vaccargiu +1Skills Natural Language
University College London, United Kingdom · University of Hohenheim, Stuttgart, Germany · University of Cagliari, Italy
QuantumQUBO Agent: Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language
Date pendingNiloy Kumar Mondal, Md Rizwan ParvezQuadratic Unconstrained Binary Optimization Quantum Annealing
Bangladesh University of Engineering and Technology · Qatar Computing Research Institute, HBKU, Doha, Qatar.

Feb 13, 2025, cs.CLJ/K move · Enter open · S save

Thinking beyond the anthropomorphic paradigm benefits LLM research

Lujain Ibrahim, Myra Cheng

University of Oxford · Stanford University

Anthropomorphism Natural Language

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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.

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Latest papers 185

Thinking beyond the anthropomorphic paradigm benefits LLM research

Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation

An Incomplete Loop: Deductive, Inductive, and Abductive Reasoning in Language Models

GitSkills: A Dataset of Agent Skills on GitHub

QuantumQUBO Agent: Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language