挑战大模型研究中的人类中心思维，探索更开放的智能路径
Thinking beyond the anthropomorphic paradigm benefits LLM research
- 分析数十万论文，发现人类化术语在大模型研究中持续增长
- 提出五种被忽视的非人类化替代方案，如机器语言推理与非人类基准评估
- 适合想突破传统范式、探索新型智能架构的研究者
人类化（anthropomorphism）是将人类特质赋予技术的无意识行为，即使在具备高级技术背景的专家中也普遍存在。本文通过分析数十万篇大模型研究论文，实证揭示了人类化术语在该领域中的广泛存在与持续增长。我们指出，尽管这类表述常具实用性，但其背后隐含的深层假设可能无意中限制了大模型的发展方向。为此，本文系统识别并剖析了贯穿大模型研发全周期的五种人类化假设，例如‘大模型必须使用自然语言进行推理’或‘评估应沿用专为人类设计的基准’。针对每项假设，我们展示尚未充分探索但前景可观的非人类化替代路径，呼吁跳出人类中心范式，为大模型的理解与优化开辟新方向。
原文摘要 · Abstract (English)
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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