神经符号AI中的符号接地：推理快捷方式的入门介绍
Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
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中文总结 AI 辅助
本文探讨神经符号AI中推理快捷方式的问题，分析其成因与影响，并提供解决方法与策略，以提升模型的可靠性和可信度。
中文摘要 AI 辅助
神经符号（NeSy）AI旨在开发深度神经网络，其预测符合先验知识编码，例如安全或结构约束。因此，它代表了可靠和可信AI最有前途的途径之一。NeSy AI的核心思想是结合神经和符号步骤：神经网络通常负责将低级输入映射到高级符号概念，而符号推理推断出与提取的概念和先验知识兼容的预测。尽管有前景，最近研究表明，当概念未直接监督时，NeSy模型可能受推理快捷方式（RSs）影响。即，它们可以通过错误地接地概念来实现高标签准确性。RSs会损害模型解释的可解释性、分布外场景的性能，因此影响可靠性。同时，除非有概念监督，否则难以检测和防止RSs，这通常并不成立。然而，关于RSs的文献零散，使研究人员和从业者难以理解和解决这一挑战性问题。本文通过提供RSs的入门介绍，讨论其成因和影响，回顾并阐明该现象的现有理论描述。最后，详细讨论处理RSs的方法，包括缓解和意识策略，并映射其优势和限制。通过将高级材料以易于消化的形式重新表述，本文旨在提供一个统一的RSs视角，以降低解决它们的门槛。最终，我们希望本文能促进可靠NeSy和可信AI模型的发展。
英文摘要
Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.g. safety or structural constraints. As such, it represents one of the most promising avenues for reliable and trustworthy AI. The core idea behind NeSy AI is to combine neural and symbolic steps: neural networks are typically responsible for mapping low-level inputs into high-level symbolic concepts, while symbolic reasoning infers predictions compatible with the extracted concepts and the prior knowledge. Despite their promise, it was recently shown that - whenever the concepts are not supervised directly - NeSy models can be affected by Reasoning Shortcuts (RSs). That is, they can achieve high label accuracy by grounding the concepts incorrectly. RSs can compromise the interpretability of the model's explanations, performance in out-of-distribution scenarios, and therefore reliability. At the same time, RSs are difficult to detect and prevent unless concept supervision is available, which is typically not the case. However, the literature on RSs is scattered, making it difficult for researchers and practitioners to understand and tackle this challenging problem. This overview addresses this issue by providing a gentle introduction to RSs, discussing their causes and consequences in intuitive terms. It also reviews and elucidates existing theoretical characterizations of this phenomenon. Finally, it details methods for dealing with RSs, including mitigation and awareness strategies, and maps their benefits and limitations. By reformulating advanced material in a digestible form, this overview aims to provide a unifying perspective on RSs to lower the bar to entry for tackling them. Ultimately, we hope this overview contributes to the development of reliable NeSy and trustworthy AI models.
发表机构
- University of Trento（特伦托大学）
- Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
- Sapienza University of Rome（罗马大学）
- University of Edinburgh（爱丁堡大学）
- Huawei Labs（华为实验室）
机构由 AI 辅助整理，请以论文原文为准。
