diff --git a/README.md b/README.md
index 9d6af39..461026c 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,8 @@
 > Large language model (LLM) based knowledge graph completion (KGC) aims to predict the missing triples in the KGs with LLMs and enrich the KGs to become better web infrastructure, which can benefit a lot of web-based automatic services. However, research about LLM-based KGC is limited and lacks effective utilization of LLM's inference capabilities, which ignores the important structural information in KGs and prevents LLMs from acquiring accurate factual knowledge. In this paper, we discuss how to incorporate the helpful KG structural information into the LLMs, aiming to achieve structrual-aware reasoning in the LLMs. We first transfer the existing LLM paradigms to structural-aware settings and further propose a knowledge prefix adapter (KoPA) to fulfill this stated goal. KoPA employs structural embedding pre-training to capture the structural information of entities and relations in the KG. Then KoPA informs the LLMs of the knowledge prefix adapter which projects the structural embeddings into the textual space and obtains virtual knowledge tokens as a prefix of the input prompt. We conduct comprehensive experiments on these structural-aware LLM-based KGC methods and provide an in-depth analysis comparing how the introduction of structural information would be better for LLM's knowledge reasoning ability.
 
 ## 🔔 News
-- **`2024-02` We preprint our Survey [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](http://arxiv.org/abs/2402.05391)  [[`Repo`](https://github.com/zjukg/KG-MM-Survey)].**
+- **`2024-07` 🎉🎉🎉 Our paper is accepted by [ACM MM 2024](https://2024.acmmm.org/) as an **oral paper**.
+- **`2024-02` 🎉🎉🎉 We preprint our Survey [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](http://arxiv.org/abs/2402.05391)  [[`Repo`](https://github.com/zjukg/KG-MM-Survey)].**
 
 ## 🌈 Model Architecture
 ![Model_architecture](figure/model.png)