Large language models (LLMs) such as ChatGPT have recently demonstrated
significant potential in mathematical abilities, providing valuable reasoning
paradigm consistent with human natural language. However, LLMs currently have
difficulty in bridging perception, language understanding and reasoning
capabilities due to incompatibility of the underlying information flow among
them, making it challenging to accomplish tasks autonomously. On the other
hand, abductive learning (ABL) frameworks for integrating the two abilities of
perception and reasoning has seen significant success in inverse decipherment
of incomplete facts, but it is limited by the lack of semantic understanding of
logical reasoning rules and the dependence on complicated domain knowledge
representation. This paper presents a novel method (ChatABL) for integrating
LLMs into the ABL framework, aiming at unifying the three abilities in a more
user-friendly and understandable manner. The proposed method uses the strengths
of LLMs' understanding and logical reasoning to correct the incomplete logical
facts for optimizing the performance of perceptual module, by summarizing and
reorganizing reasoning rules represented in natural language format. Similarly,
perceptual module provides necessary reasoning examples for LLMs in natural
language format. The variable-length handwritten equation deciphering task, an
abstract expression of the Mayan calendar decoding, is used as a testbed to
demonstrate that ChatABL has reasoning ability beyond most existing
state-of-the-art methods, which has been well supported by comparative studies.
To our best knowledge, the proposed ChatABL is the first attempt to explore a
new pattern for further approaching human-level cognitive ability via natural
language interaction with ChatGPT.

该研究论文提出了一种将大型语言模型（LLMs）与溯因学习（ABL）框架相结合的新方法（ChatABL），旨在以更易于理解的方式统一感知、语言理解和推理能力，进一步接近人类水平的认知能力。该方法通过将自然语言格式中表示的推理规则进行总结和重新组织，利用 LLM 的理解和逻辑推理的优势来校正不完整的逻辑事实，以优化感知模块的性能，并提供必要的推理示例，以自然语言的形式为 LLMs 提供帮助。研究结果表明，ChatABL 在抽象表达上超越了大多数现有的最先进方法，并具有超越人类的推理能力。

ChatABL: 基于自然语言交互的诱因推理学习与 ChatGPT

ChatABL: Abductive Learning via Natural Language Interaction with  ChatGPT

Explaining neural network models is a challenging task that remains unsolved
in its entirety to this day. This is especially true for high dimensional and
complex data. With the present work, we introduce two notions for conceptual
views of a neural network, specifically a many-valued and a symbolic view. Both
provide novel analysis methods to enable a human AI analyst to grasp deeper
insights into the knowledge that is captured by the neurons of a network. We
test the conceptual expressivity of our novel views through different
experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to
which extent the views allow to quantify the conceptual similarity of different
learning architectures. Finally, we demonstrate how conceptual views can be
applied for abductive learning of human comprehensible rules from neurons. In
summary, with our work, we contribute to the most relevant task of globally
explaining neural networks models.

本研究介绍了两种神经网络的概念视图，即多值视图和符号视图，并通过在 ImageNet 和 Fruit-360 数据集上的实验测试了其概念表达能力。此外，论文还展示了这些视图可以量化不同学习架构之间的概念相似度，并演示了如何将概念视图应用于从神经元中归纳学习人类可理解的规则，从而为全局解释神经网络模型做出贡献。

神经网络中的正式概念视图

Formal Conceptual Views in Neural Networks

Mathematical reasoning recently has been shown as a hard challenge for neural
systems. Abilities including expression translation, logical reasoning, and
mathematics knowledge acquiring appear to be essential to overcome the
challenge. This paper demonstrates that some abilities can be achieved through
abductive combination with discrete systems that have been programmed with
human knowledge. On a mathematical reasoning dataset, we adopt the recently
proposed abductive learning framework, and propose the ABL-Sym algorithm that
combines the Transformer neural models with a symbolic mathematics library.
ABL-Sym shows 9.73% accuracy improvement on the interpolation tasks and 47.22%
accuracy improvement on the extrapolation tasks, over the state-of-the-art
approaches. Online demonstration: this http URL

通过离散型系统和抽象学习框架的组合，提出了 ABL-Sym 算法，将 Transformer 神经模型与符号数学库相结合，从而在数学推理数据集上显示出显著的准确性提高。