Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are actually unreasonable, while little works have been done on interpretability evaluation for them. In this paper, we propose a unified framework to quantitatively evaluate the interpretability of multi-hop reasoning models so as to advance their development. In specific, we define three metrics including path recall, local interpretability, and global interpretability for evaluation, and design an approximate strategy to calculate them using the interpretability scores of rules. Furthermore, we manually annotate all possible rules and establish a Benchmark to detect the Interpretability of Multi-hop Reasoning (BIMR). In experiments, we run nine baselines on our benchmark. The experimental results show that the interpretability of current multi-hop reasoning models is less satisfactory and is still far from the upper bound given by our benchmark. Moreover, the rule-based models outperform the multi-hop reasoning models in terms of performance and interpretability, which points to a direction for future research, i.e., we should investigate how to better incorporate rule information into the multi-hop reasoning model. Our codes and datasets can be obtained from https://github.com/THU-KEG/BIMR.
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；ByteDance Inc.（字节跳动公司）
METER: Multi-modal Evidence-based Thinking and Explainable Reasoning -- Algorithm and Benchmark
Institute of Artificial Intelligence (TeleAI), China Telecom（电信人工智能研究院）；Institute of Artificial Intelligence and Robotics(IAIR), Xi’an Jiaotong University（人工智能与机器人研究院）；Advanced Technique of Artificial Intelligence(ATAI), Chongqing University of Technology（人工智能先进技术研究院）
HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering
University of Macau（澳门大学）；Shenzhen Kaihong Digital Industry Development Co., Ltd.（深圳开鸿数字产业发展有限公司）；University of New South Wales（新南威尔士大学）；Zhejiang University（浙江大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Institute for Artificial Intelligence, University of Stuttgart, Germany（人工智能研究所，斯图加特大学）；School of Informatics University of Edinburgh Edinburgh, UK（信息学院，爱丁堡大学）；University of Southampton, UK（南安普顿大学）；Stanford University（斯坦福大学）
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations
Tsinghua University（清华大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；KIRC, Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能研究院知识图谱与推理中心（KIRC））；Beijing National Research Center for Information Science and Technology（北京国家信息科学与技术研究中心）
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
Tsinghua University（清华大学）；University of Southern California（南加利福尼亚大学）
可解释性和有效性是构建推荐系统的两个关键方面。以前的努力主要集中在合并边信息，以实现更好的推荐性能。然而，这些方法具有一些弱点：（1）基于神经网络的嵌入方法的预测难以解释和调试;（2）基于符号、图形的方法（例如，基于Meta路径的模型）需要人工努力和领域知识来定义模式和规则，并且忽略项目关联类型（例如，可替换的和互补的）。在本文中，我们提出了一种新的联合学习框架集成\textit{induction of explainable rules from knowledge graph}与\textit{construction of a rule-guided neural recommendation model}。该框架鼓励两个模块相互补充，以产生有效的和可解释的推荐：1）归纳规则，从以项目为中心的知识图谱中挖掘，总结常见的多跳关系模式，用于推断不同的项目关联，并为模型预测提供人类可读的解释; 2）推荐模块可以通过归纳规则进行扩充，从而在处理冷启动问题时具有更好的泛化能力。大量的实验\footnote{Code and data can be found at: \url{https://github.com/THUIR/RuleRec}}表明，我们提出的方法已经取得了显着的改善，在现实世界的数据集上的基线项目推荐。我们的模型在通过将项目名称链接到相关实体而生成的“嘈杂”项目知识…
TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery
Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高等研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Tsukuba（筑波大学）；Central China Normal University（华中师范大学）
SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学 Gallagher人工智能学院）；Beijing Key Laboratory of Research on Large Models（北京大模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索工程研究中心）
