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@@ -314,7 +314,7 @@ <h4><alert>Question Answering</alert></h4>
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                 <div>We have developed question answering systems under different scenarios. 
                 For Machine Reading Comprehension (MRC), we proposed different methods to improve the accuracy of answering questions over passages [<a href="https://ojs.aaai.org/index.php/AAAI/article/view/6458/6314">AAAI'20</a>; <a href="https://aclanthology.org/2023.findings-acl.391.pdf">ACL'23</a>]. 
-                For Knowledge Graph Question Answering (KGQA), we investegated and improved different modules in the QA framework, which achieves SOTA results in a variety of scenarios (e.g., simple questions [<a href="https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5443&context=sis_research">IJCAI 2019</a>], complex questions [<a href="https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5939&context=sis_research">ICDM 2019</a>; <a href="https://aclanthology.org/2020.acl-main.91.pdf">ACL'20</a>; <a href="https://arxiv.org/pdf/2306.06872">ACL'23</a>], conversational questions [<a href="https://aclanthology.org/2021.acl-long.255.pdf">ACL'21</a>]).
+                For Knowledge Graph Question Answering (KGQA), we investegated and improved different modules in the QA framework, which achieves SOTA results in a variety of scenarios (e.g., simple questions [<a href="https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5443&context=sis_research">IJCAI 2019</a>], complex questions [<a href="https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5939&context=sis_research">ICDM'19</a>; <a href="https://aclanthology.org/2020.acl-main.91.pdf">ACL'20</a>; <a href="https://arxiv.org/pdf/2306.06872">ACL'23</a>], conversational questions [<a href="https://aclanthology.org/2021.acl-long.255.pdf">ACL'21</a>]).
                 For Question Answering with Databases, we explored to generate complex questions in a low-resource condition [<a href="https://arxiv.org/pdf/2310.08395">EMNLP'23</a>] and align Large Language Models (LLMs) to a domain-specific database [<a href="https://arxiv.org/pdf/2402.16567">ArXiv</a>].
                 For Multi-modal Question Answering, we discussed the applications of LLMs and its safety issue [<a href="http://arxiv.org/abs/2311.09050.pdf">MM'23</a>; <a href="https://arxiv.org/pdf/2402.00357">IJCAI'24</a>;<a href="https://arxiv.org/pdf/2311.17600">ECCV'24</a>].
 
@@ -347,6 +347,25 @@ <h2 id="confpapers">Selected Papers (<a href="https://scholar.google.com/citatio
             (* indicates equal contribution, # indicates corresponding author)
 
             <div class="paper">
+
+            <ul>
+                <li>
+                <a href="https://arxiv.org/pdf/2311.17600">Query-relevant images jailbreak large multi-modal models</a><br>
+                Xin Liu, Yichen Zhu, Yunshi Lan#, Chao Yang#, Yu Qiao<br>
+                ECCV, 2024 <br>
+                <br>
+                </li>
+            </ul>
+
+            <ul>
+                <li>
+                <a href="https://arxiv.org/pdf/2402.00357">Safety of Multimodal Large Language Models on Images and Text</a><br>
+                Xin Liu, Yichen Zhu, Yunshi Lan#, Chao Yang#, Yu Qiao<br>
+                IJCAI, survey paper, 2024 <br>
+                <br>
+                </li>
+            </ul>
+
             <ul>
                 <li>
                 FinQA: A Training-free Dynamic Knowledge Graph Question Answering System in Finance with LLM-based Revision<br>
@@ -383,6 +402,16 @@ <h2 id="confpapers">Selected Papers (<a href="https://scholar.google.com/citatio
                 </li>
             </ul>
 
+            <ul>
+                <li>
+                <a href="https://arxiv.org/pdf/2310.16535">R3 Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context</a><br>
+                Qingyuan Tian, Hanlun Zhu, Lei Wang, Yang Li, Yunshi Lan<br>
+                EMNLP Finding, 2023 <br>
+                <a href="./resources/bibtex/emnlp2023_kbqg.txt">[BibTex]</a>
+                <br>
+                </li>
+            </ul>
+
             <ul>
                 <li>
                 <a href="http://arxiv.org/abs/2311.09050.pdf">Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts</a><br>