NeSyCoCo：用于组合泛化的神经符号概念组合器
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization
AI总结：
提出神经符号框架NeSyCoCo，利用大语言模型生成符号表示并映射至可微神经计算，通过依存结构增强输入、分布式词表示链接谓词与神经模块及软组合对齐推理，在组合泛化基准上达到最优。
摘要：
组合泛化对于人工智能智能体解决复杂的视觉-语言推理任务至关重要。神经符号方法在捕获组合结构方面展现了潜力，但面临关键挑战：(a) 依赖预定义谓词进行符号表示，限制了适应性；(b) 难以从原始数据中提取谓词；(c) 使用不可微操作来组合基础概念。为解决这些问题，我们提出了NeSyCoCo，这是一个利用大语言模型（LLMs）生成符号表示并将其映射到可微神经计算的神经符号框架。NeSyCoCo引入了三项创新：(a) 用依存结构增强自然语言输入，以加强与符号表示的对齐；(b) 采用分布式词表示，将多样的、受语言学启发的逻辑谓词链接到神经模块；(c) 使用归一化谓词分数的软组合来对齐符号推理与可微推理。我们的框架在ReaSCAN和CLEVR-CoGenT组合泛化基准上取得了最先进的结果，并在CLEVR-SYN基准中展示了对新概念的稳健性能。
英文摘要：
Compositional generalization is crucial for artificial intelligence agents to solve complex vision-language reasoning tasks. Neuro-symbolic approaches have demonstrated promise in capturing compositional structures, but they face critical challenges: (a) reliance on predefined predicates for symbolic representations that limit adaptability, (b) difficulty in extracting predicates from raw data, and (c) using non-differentiable operations for combining primitive concepts. To address these issues, we propose NeSyCoCo, a neuro-symbolic framework that leverages large language models (LLMs) to generate symbolic representations and map them to differentiable neural computations. NeSyCoCo introduces three innovations: (a) augmenting natural language inputs with dependency structures to enhance the alignment with symbolic representations, (b) employing distributed word representations to link diverse, linguistically motivated logical predicates to neural modules, and (c) using the soft composition of normalized predicate scores to align symbolic and differentiable reasoning. Our framework achieves state-of-the-art results on the ReaSCAN and CLEVR-CoGenT compositional generalization benchmarks and demonstrates robust performance with novel concepts in the CLEVR-SYN benchmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
