EventRAG: Supportive Event Retrieval on Hypergraph for Future Forecasting
Authors: Zhengwei Tao, Zhi Jin, pu wu, Xiaoying Bai, Haiyan Zhao, Jia Li, Xiancai Chen, Linyu Li, Chongyang Tao
Accepted at: acl25


Reviewer paper summaries: This paper proposes the EventRAG framework for future event prediction. Unlike traditional retrieval methods based on textual similarity or structured entity relevance, the proposed approach focuses on retrieving evidence that supports future prediction. To achieve this, the authors introduce event hypergraph retrieval and the maximization of expected event coverage to retrieve supportive content. Finally, an event-oriented agentic reasoning module is developed to perform inference.

While the paper is motivated by an important and challenging problem, it has several critical issues that need to be addressed:
Summary of strengths: 1. The paper is well-written.  
2. The proposed Maximum Expected Event Cover has been demonstrated to be effective both theoretically and experimentally.  
3. The authors achieve SOTA results compared to other RAG models on the PROPHET dataset.
Summary of weaknesses: 1. Supportiveness is neither clearly defined nor validated effectively. Although the authors aim to distinguish supportiveness-based retrieval from text similarity and entity-centric retrieval, the concept of supportiveness remains ambiguous. In the methodology section, the authors directly use LLMs to score event-question pairs, but my concern is how the scores obtained in this manner differ from metrics such as relevance or similarity.?

2. The authors evaluate their approach on only one dataset, which limits the reliability of the paper. While the authors claim that the dataset is sufficient to validate the evaluation of supportiveness, no further explanation or justification is provided. Considering that relevance and supportiveness are not clearly distinguished, and that the method lacks specific designs targeting future forecasting (e.g., temporal reasoning), could the proposed approach generalize and perform well on other RAG-based datasets?
Comments, suggestions and typos: 1. Line 268, misuse of periods.
2. Line 339, the abbreviation "IP" is not explained.
3. Line 399, missing spaces.
Overall assessment scores: 3
Metareview score: 3.5
Metareview: This paper presents EventRAG, a novel event-oriented framework designed for future forecasting tasks, which constructs an event hypergraph index on a knowledge base and formulates event retrieval as a maximum expected event cover program, while also leveraging a multi-step agentic reasoning process to iteratively acquire knowledge.

The incorporation of an event hypergraph index enables a more nuanced capture of the complex, multi-party correlations between events, and experimental results indicate significant improvements over baselines, highlighting the effectiveness in handling complex forecasting task of the proposed method. Additionally, the dataset/metric issues also limit the generalizability and effectiveness of the proposed method. However, Ithere are no doubt for all reviewers to accept this work as a borderline work.
Summary of reasons to publish: 1. A novel concept for supporting event retrieval is introduced, which deviates from traditional approaches focused on text similarity or entity relationships, and demonstrates greater efficacy in tackling future prediction tasks. Furthermore, the incorporation of an event hypergraph index enables a more nuanced capture of the complex, multi-party correlations between events
2. Experimental results indicate significant improvements over baseline models, highlighting the effectiveness in handling complex forecasting tasks.