diff --git a/_config.yml b/_config.yml
index 6f5072d..0c0c59a 100644
--- a/_config.yml
+++ b/_config.yml
@@ -83,10 +83,10 @@ author:
   name             : "Bale Chen"
   avatar           : "avatar.png"
   cn_name          : "Also: 陈丙森 (Chen Bing Sen)"
-  bio              : "PhD student at NYU \n NLP Researcher"
+  bio              : "PhD student at NYU
+  NLP Researcher"
   location         : "Shanghai, China"
   employer         : 
-  # pubmed           : "https://www.ncbi.nlm.nih.gov/pubmed/?term=john+snow"
   # googlescholar    : "http://yourfullgooglescholarurl.com"
   email            :  "bale.chen@nyu.edu"
   researchgate     :  # example: "https://www.researchgate.net/profile/yourprofile"
diff --git a/_data/navigation.yml b/_data/navigation.yml
index f27eddc..63596e3 100644
--- a/_data/navigation.yml
+++ b/_data/navigation.yml
@@ -16,7 +16,7 @@ main:
   #   url: /year-archive/
 
   - title: "CV"
-    url: /files/CV_Bale_Chen_1215.pdf
+    url: /files/Bale_Chen_Simplified_PhD_CV_Jun2024.pdf
 
   - title: "More"
     url: /more/
diff --git a/_pages/about.md b/_pages/about.md
index 15a7f65..20bcb94 100644
--- a/_pages/about.md
+++ b/_pages/about.md
@@ -8,10 +8,11 @@ redirect_from:
   - /about.html
 ---
 
-Hi! I am an incoming CS PhD student at [NYU Courant](https://cims.nyu.edu/dynamic/) and [NYU Shanghai](https://shanghai.nyu.edu), mentored by Professor [Chen Zhao](http://www.chenz.umiacs.io/). I received my B.S. degree in Data Science (concentration in Artificial Intelligence) from NYU Shanghai, where I was supervised by Professor [Wilson Tam](https://shanghai.nyu.edu/academics/faculty/directory/yik-cheung-wilson-tam). My research interests are in **Retrieval-Augmented Langauge Models** and **Controllable Text Generation**. 
+Hi! I am an incoming CS PhD student at [NYU Courant](https://cims.nyu.edu/dynamic/) and [NYU Shanghai](https://shanghai.nyu.edu), mentored by Professor [Chen Zhao](http://www.chenz.umiacs.io/). Previously, I received my B.S. degree in Data Science (concentration in Artificial Intelligence) from NYU Shanghai, where I was supervised by Professor [Wilson Tam](https://shanghai.nyu.edu/academics/faculty/directory/yik-cheung-wilson-tam). My research interests are in **Retrieval-Augmented Langauge Models** and **Controllable Text Generation**. 
 
 News
 ======
 
-* **[March 2024]** I am attending NAACL 2024 to present our work on [](https://arxiv.org/abs/2405.17893) in collaboration with Xiaocheng Yang and Professor Wilson Tam!
+* **[March 2024]** I am attending NAACL 2024 to present our work [ProPer](https://arxiv.org/abs/2405.17893) in collaboration with Xiaocheng Yang and Professor Wilson Tam!
+
 
diff --git a/_publications/20240313-prolog.md b/_publications/20240313-prolog.md
index b07ab37..d4f9bc7 100644
--- a/_publications/20240313-prolog.md
+++ b/_publications/20240313-prolog.md
@@ -2,9 +2,7 @@
 title: "Arithmetic Reasoning with LLM: Prolog Generation & Permutation"
 collection: publications
 permalink: /publication/20240313-prolog
-excerpt: '<p> <a href="https://arxiv.org/abs/2405.17893" style="color:#51ADC8;">Paper</a> <a href="https://github.com/yxc-cyber/ProPer" style="color:#51ADC8;">Code</a><br />Xiaocheng Yang, <b>Bingsen Chen</b>, Yik-Cheng Tam</p>'
-date: 2024-03-13
-venue: 'NAACL 2024'
+excerpt: '<p> [<a href="https://arxiv.org/abs/2405.17893" style="color:#51ADC8;">Paper</a>] [<a href="https://github.com/yxc-cyber/ProPer" style="color:#51ADC8;">Code</a>]<br />[NAACL 2024] Xiaocheng Yang, <b>Bingsen Chen</b>, Yik-Cheng Tam</p>'
 ---
 
 Instructing large language models (LLMs) to solve elementary school math problems has shown great success using Chain of Thought (CoT). However, the CoT approach relies on an LLM to generate a sequence of arithmetic calculations which can be prone to cascaded calculation errors. We hypothesize that an LLM should focus on extracting predicates and generating symbolic formulas from the math problem description so that the underlying calculation can be done via an external code interpreter. We investigate using LLM to generate Prolog programs to solve mathematical questions. Experimental results show that our Prolog-based arithmetic problem-solving outperforms CoT generation in the GSM8K benchmark across three distinct LLMs. In addition, given the insensitive ordering of predicates and symbolic formulas in Prolog, we propose to permute the ground truth predicates for more robust LLM training via data augmentation.
diff --git a/files/Bale_Chen_Simplified_PhD_CV_Jun2024.pdf b/files/Bale_Chen_Simplified_PhD_CV_Jun2024.pdf
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index 0000000..2676ae6
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diff --git a/files/CV_Bale_Chen_1215.pdf b/files/CV_Bale_Chen_1215.pdf
deleted file mode 100644
index 7965b19..0000000
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