Update README.md

Author: 14H034160212

README.md

@@ -131,29 +131,30 @@ If the paper and code are helpful, please kindly cite our paper:
 ```
 @inproceedings{bao-etal-2024-abstract,
     title = "{A}bstract {M}eaning {R}epresentation-Based Logic-Driven Data Augmentation for Logical Reasoning",
-    author = "Bao, Qiming  and
-      Peng, Alex  and
+    author = {Bao, Qiming  and
+      Peng, Alex Yuxuan  and
       Deng, Zhenyun  and
       Zhong, Wanjun  and
-      Gendron, Gael  and
+      Gendron, Ga{\"e}l  and
       Pistotti, Timothy  and
-      Tan, Neset  and
+      Tan, Ne{\c{s}}et  and
       Young, Nathan  and
       Chen, Yang  and
       Zhu, Yonghua  and
       Denny, Paul  and
       Witbrock, Michael  and
-      Liu, Jiamou",
+      Liu, Jiamou},
     editor = "Ku, Lun-Wei  and
       Martins, Andre  and
       Srikumar, Vivek",
-    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
     month = aug,
     year = "2024",
-    address = "Bangkok, Thailand and virtual meeting",
+    address = "Bangkok, Thailand",
     publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2024.findings-acl.353",
+    url = "https://aclanthology.org/2024.findings-acl.353/",
+    doi = "10.18653/v1/2024.findings-acl.353",
     pages = "5914--5934",
-    abstract = "Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges when gathering reliable data from the web to build comprehensive training datasets, subsequently affecting performance on downstream tasks. To address this, we introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph, a structured semantic representation that encapsulates the logical structure of the sentence, upon which operations are performed to generate logically modified AMR graphs. The modified AMR graphs are subsequently converted back into text to create augmented data. Notably, our methodology is architecture-agnostic and enhances both generative large language models, such as GPT-3.5 and GPT-4, through prompt augmentation, and discriminative large language models through contrastive learning with logic-driven data augmentation. Empirical evidence underscores the efficacy of our proposed method with improvement in performance across seven downstream tasks, such as reading comprehension requiring logical reasoning, textual entailment, and natural language inference. Furthermore, our method leads on the ReClor leaderboard. The source code and data are publicly available",
+    abstract = "Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges when gathering reliable data from the web to build comprehensive training datasets, subsequently affecting performance on downstream tasks. To address this, we introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph, a structured semantic representation that encapsulates the logical structure of the sentence, upon which operations are performed to generate logically modified AMR graphs. The modified AMR graphs are subsequently converted back into text to create augmented data. Notably, our methodology is architecture-agnostic and enhances both generative large language models, such as GPT-3.5 and GPT-4, through prompt augmentation, and discriminative large language models through contrastive learning with logic-driven data augmentation. Empirical evidence underscores the efficacy of our proposed method with improvement in performance across seven downstream tasks, such as reading comprehension requiring logical reasoning, textual entailment, and natural language inference. Furthermore, our method leads on the ReClor leaderboard at https://eval.ai/web/challenges/challenge-page/503/leaderboard/1347. The source code and data are publicly available at https://github.com/Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Augmentation-for-Representation-Learning."
 }
 ```