Update format and cv

Author: Keyu-He

_bibliography/papers.bib

@@ -2,6 +2,7 @@
 ---
 
 @article{huihan2024culture,
+  abbr={ICLR 2025},
   title={Attributing Culture-Conditioned Generations to Pretraining Corpora},
   author={Huihan Li* and Arnav Goel* and Keyu He and Xiang Ren},
   year={2025},
@@ -14,6 +15,7 @@ @article{huihan2024culture
 }
 
 @article{keyu2025explanations,
+  abbr={ACL Findings 2025},
   title={ELI-Why: Evaluating the Pedagogical Utility of LLM Explanations},
   author={Brihi Joshi* and Keyu He* and Sahana Ramnath and Sadra Sabouri and Kaitlyn Zhou and Souti Chattopadhyay and Swabha Swayamdipta and Xiang Ren},
   year={2025},
@@ -25,6 +27,7 @@ @article{keyu2025explanations
 }
 
 @article{keyu2025vlm,
+  abbr={PREPRINT},
   title={Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations},
   author={Keyu He and Tejas Srinivasan and Brihi Joshi and Xiang Ren and Jesse Thomason and Swabha Swayamdipta},
   year={2025},
@@ -35,6 +38,7 @@ @article{keyu2025vlm
 }
 
 @article{enhancing-debugging,
+  abbr={Tech Report},
   title={Enhancing Debugging Skills of LLMs with Prompt Engineering},
   author={Keyu He* and Max Li* and Joseph Liu*},
   abstract={This paper presents a comprehensive study on improving the debugging capabilities of Large Language Models (LLMs) like GPT-3.5, focusing on the application of prompt engineering techniques. We explore the efficacy of few-shot learning, chain-of-thought prompting, and a baseline zero-shot model in enhancing LLMs' ability to debug code. Utilizing static and dynamic evaluation metrics, the study rigorously assesses the debugging proficiency of these models. By introducing different types of bugs, including procedural and language model-generated errors, and applying varied prompting strategies, we provide a deeper understanding of LLMs' debugging capabilities. The results provide insights into the limitation of debugging capabilities of GPT-3.5 Turbo, even with the assistance of various prompting techniques. Source code of our evaluation method and bug generation techniques are in GitHub repository: https://github.com/FrankHe2002/CSCI499FinalProject.},

_sass/_themes.scss

@@ -9,8 +9,8 @@
   --global-code-bg-color: #{$code-bg-color-light};
   --global-text-color: #{$black-color};
   --global-text-color-light: #{$grey-color};
-  --global-theme-color: #1e4fff; // #{$purple-color};
-  --global-hover-color: #0d33bb; //#{$purple-color};
+  --global-theme-color: #8b1538;//#{$purple-color};
+  --global-hover-color: #6610f2;//#{$purple-color};
   --global-hover-text-color: #{$white-color};
   --global-footer-bg-color: #{$grey-color-dark};
   --global-footer-text-color: #{$grey-color-light};