Update README.md

README.md

@@ -19,11 +19,7 @@ size_categories:
 ---
 
 # EmoBench
-> This is the official repository for our paper ["EmoBench: Evaluating the Emotional Intelligence of Large Language Models"](https://arxiv.org/abs/2402.12071)
-
-<img src="https://img.shields.io/badge/Venue-ACL--24-278ea5" alt="venue"/> <img src="https://img.shields.io/badge/Status-Under Review-success" alt="status"/> <img src="https://img.shields.io/badge/Contributions-Welcome-red"> <img src="https://img.shields.io/badge/Last%20Updated-2024--03--11-2D333B" alt="update"/>
-
-![EmoBench](EmoBench.jpg)
+> This is the official repository for our ACL 2024 paper ["EmoBench: Evaluating the Emotional Intelligence of Large Language Models"](https://arxiv.org/abs/2402.12071)
 
 ## Overview
 
@@ -34,6 +30,8 @@ The dataset includes **400 hand-crafted scenarios** in English and Chinese, stru
 - **Emotional Understanding (EU):** Recognizing emotions and their causes in complex scenarios.
 - **Emotional Application (EA):** Recommending effective emotional responses or actions in emotionally charged dilemmas.
 
+![EmoBench](EmoBench.jpg)
+
 ## Key Features
 
 - **Psychology-based Design:** Grounded in established theories of Emotional Intelligence (e.g., Salovey & Mayer, Goleman).
@@ -63,9 +61,28 @@ For code regarding evaluation, please visit [our repository](https://github.com/
 ## Citation
 If you find our work useful for your research, please kindly cite our paper as follows:
 ```
-@article{EmoBench2024,
-     title={EmoBench: Evaluating the Emotional Intelligence of Large Language Models}, 
-      author={Sahand Sabour and Siyang Liu and Zheyuan Zhang and June M. Liu and Jinfeng Zhou and Alvionna S. Sunaryo and Juanzi Li and Tatia M. C. Lee and Rada Mihalcea and Minlie Huang},
-			year={2024},
-      eprint={2402.12071},
-      archivePrefix={arXiv},
+@inproceedings{sabour-etal-2024-emobench,
+    title = "{E}mo{B}ench: Evaluating the Emotional Intelligence of Large Language Models",
+    author = "Sabour, Sahand  and
+      Liu, Siyang  and
+      Zhang, Zheyuan  and
+      Liu, June  and
+      Zhou, Jinfeng  and
+      Sunaryo, Alvionna  and
+      Lee, Tatia  and
+      Mihalcea, Rada  and
+      Huang, Minlie",
+    editor = "Ku, Lun-Wei  and
+      Martins, Andre  and
+      Srikumar, Vivek",
+    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.acl-long.326",
+    doi = "10.18653/v1/2024.acl-long.326",
+    pages = "5986--6004",
+    abstract = "Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelligence (EI) is considerably limited. Existing benchmarks have two major shortcomings: first, they mainly focus on emotion recognition, neglecting essential EI capabilities such as emotion management and thought facilitation through emotion understanding; second, they are primarily constructed from existing datasets, which include frequent patterns, explicit information, and annotation errors, leading to unreliable evaluation. We propose EmoBench, a benchmark that draws upon established psychological theories and proposes a comprehensive definition for machine EI, including Emotional Understanding and Emotional Application. EmoBench includes a set of 400 hand-crafted questions in English and Chinese, which are meticulously designed to require thorough reasoning and understanding. Our findings reveal a considerable gap between the EI of existing LLMs and the average human, highlighting a promising direction for future research. Our code and data are publicly available at https://github.com/Sahandfer/EmoBench.",
+}
+```