303位研究者调查发现，当前NLP系统很少被视作真正智能。
Research Community Perspectives on "Intelligence" and Large Language Models
- 通过问卷调研厘清学界对智能的共识标准
- 仅29%认为现有NLP系统具备智能，16.2%以发展智能为目标
- 通用性、适应性与推理能力是公认的智能三要素
尽管自然语言处理（NLP）研究普遍采用“人工智能”框架，但学者对“智能”的具体含义仍不清晰。为此，我们开展了一项调查，探讨研究者对“智能”的理解及其在研究议程中的作用。调查共收集来自NLP、机器学习（ML）、认知科学、语言学和神经科学等领域的303位研究人员的完整反馈。结果识别出学界最认可的三项智能标准：泛化能力、适应性与推理能力。研究显示，仅有29%的研究者认为当前NLP系统具备智能；仅16.2%将构建智能系统作为研究目标，且这部分人更倾向于认为现有系统已具智能。
原文摘要 · Abstract (English)
Despite the widespread use of ''artificial intelligence'' (AI) framing in Natural Language Processing (NLP) research, it is not clear what researchers mean by ''intelligence''. To that end, we present the results of a survey on the notion of ''intelligence'' among researchers and its role in the research agenda. The survey elicited complete responses from 303 researchers from a variety of fields including NLP, Machine Learning (ML), Cognitive Science, Linguistics, and Neuroscience. We identify 3 criteria of intelligence that the community agrees on the most: generalization, adaptability, & reasoning. Our results suggests that the perception of the current NLP systems as ''intelligent'' is a minority position (29%). Furthermore, only 16.2% of the respondents see developing intelligent systems as a research goal, and these respondents are more likely to consider the current systems intelligent.
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