Geometry Problem Solving (GPS), which is a classic and challenging math
problem, has attracted much attention in recent years. It requires a solver to
comprehensively understand both text and diagram, master essential geometry
knowledge, and appropriately apply it in reasoning. However, existing works
follow a paradigm of neural machine translation and only focus on enhancing the
capability of encoders, which neglects the essential characteristics of human
geometry reasoning. In this paper, inspired by dual-process theory, we propose
a Dual-Reasoning Geometry Solver (DualGeoSolver) to simulate the dual-reasoning
process of humans for GPS. Specifically, we construct two systems in
DualGeoSolver, namely Knowledge System and Inference System. Knowledge System
controls an implicit reasoning process, which is responsible for providing
diagram information and geometry knowledge according to a step-wise reasoning
goal generated by Inference System. Inference System conducts an explicit
reasoning process, which specifies the goal in each reasoning step and applies
the knowledge to generate program tokens for resolving it. The two systems
carry out the above process iteratively, which behaves more in line with human
cognition. We conduct extensive experiments on two benchmark datasets, GeoQA
and GeoQA+. The results demonstrate the superiority of DualGeoSolver in both
solving accuracy and robustness from explicitly modeling human reasoning
process and knowledge application.

提出了一个名为 DualGeoSolver 的双重推理几何求解器，通过模拟人类的双重推理过程，从对人类推理过程和知识应用的显式建模中提高求解几何问题的准确率和鲁棒性。

通过模拟人类双重推理过程学习解决几何问题

Learning to Solve Geometry Problems via Simulating Human Dual-Reasoning  Process

In recent years, the rapid development of artificial intelligence technology,
especially the emergence of large language models (LLMs) such as ChatGPT, has
presented significant prospects for application in the field of education. LLMs
possess the capability to interpret knowledge, answer questions, and consider
context, thus providing support for dialogic teaching to students. Therefore,
an examination of the capacity of LLMs to effectively fulfill instructional
roles, thereby facilitating student learning akin to human educators within
dialogic teaching scenarios, is an exceptionally valuable research topic. This
research recruited 34 undergraduate students as participants, who were randomly
divided into two groups. The experimental group engaged in dialogic teaching
using ChatGPT, while the control group interacted with human teachers. Both
groups learned the histogram equalization unit in the information-related
course "Digital Image Processing". The research findings show comparable scores
between the two groups on the retention test. However, students who engaged in
dialogue with ChatGPT exhibited lower performance on the transfer test.
Electroencephalography data revealed that students who interacted with ChatGPT
exhibited higher levels of cognitive activity, suggesting that ChatGPT could
help students establish a knowledge foundation and stimulate cognitive
activity. However, its strengths on promoting students. knowledge application
and creativity were insignificant. Based upon the research findings, it is
evident that ChatGPT cannot fully excel in fulfilling teaching tasks in the
dialogue teaching in information related courses. Combining ChatGPT with
traditional human teachers might be a more ideal approach. The synergistic use
of both can provide students with more comprehensive learning support, thus
contributing to enhancing the quality of teaching.

最近几年，人工智能技术的迅猛发展，特别是诸如 ChatGPT 等大型语言模型的出现，为教育领域的应用提供了重要前景。然而，对于 LLMs 来有效地履行教学角色以促进学生学习，从而实现与人类教育工作者类似的对话式教学情景，仍然是一个非常有价值的研究课题。本研究招募了 34 名本科生作为参与者，随机分成两组。实验组使用 ChatGPT 进行对话式教学，而对照组与人类教师互动。两组学习了信息相关课程 “数字图像处理” 中的直方图均衡化单元。研究结果显示，两组在记忆测试中得分相当。然而，与 ChatGPT 交互的学生在迁移测试中表现较差。脑电图数据显示，与 ChatGPT 互动的学生展示出更高水平的认知活动，表明 ChatGPT 可以帮助学生建立知识基础并刺激认知活动。然而，它在促进学生知识应用和创造力方面的优势不显著。基于研究结果，可以明确 ChatGPT 在信息相关课程的对话式教学中不能完全胜任教学任务。结合 ChatGPT 与传统人类教师可能是更理想的方法。两者的协同使用可以为学生提供更全面的学习支持，从而提高教学质量。

应用 ChatGPT 进行对话教学的有效性研究：基于脑电图的调查

Investigation of the effectiveness of applying ChatGPT in Dialogic  Teaching Using Electroencephalography

Utilities of knowledge graphs (KGs) depend on their qualities. A KG that is
of poor quality not only has little applicability but also leads to some
unexpected errors. Therefore, quality evaluation for KGs is crucial and
indispensable. Existing methods design many quality dimensions and calculate
metrics in the corresponding dimensions based on details (i.e., raw data and
graph structures) of KGs for evaluation. However, there are two major issues.
On one hand, they consider the details as public information, which exposes the
raw data and graph structures. These details are strictly confidential because
they involve commercial privacy or others in practice. On the other hand, the
existing methods focus on how much knowledge KGs have rather than KGs'
practicability. To address the above problems, we propose a knowledge graph
quality evaluation framework under incomplete information (QEII). The quality
evaluation problem is transformed into an adversarial game, and the relative
quality is evaluated according to the winner and loser. Participants of the
game are KGs, and the adversarial gameplay is to question and answer (Q&A). In
the QEII, we generate and train a question model and an answer model for each
KG. The question model of a KG first asks a certain number of questions to the
other KG. Then it evaluates the answers returned by the answer model of the
other KG and outputs a percentage score. The relative quality is evaluated by
the scores, which measures the ability to apply knowledge. Q&A messages are the
only information that KGs exchange, without exposing any raw data and graph
structure. Experimental results on two pairs of KGs demonstrate that, comparing
with baselines, the QEII realizes a reasonable quality evaluation from the
perspective of third-party evaluators under incomplete information.

提出了一种基于不完全信息的知识图谱质量评估框架（QEII），通过对两个参与者的对抗游戏中问题和回答的质量来评估知识图谱的相对质量，实现了第三方评估者能够合理评估知识在应用中的质量。