fix indent error in paper list

_data/paperlist.yml

@@ -4796,21 +4796,21 @@ papers:
 
 - title: "LLMatic Neural Architecture Search via Large Language Models and Quality Diversity Optimization"
   authors: 
-    - "Muhammad U. Nasir"
-    - "Sam Earle"
-    - "Julian Togelius"
-    - "Steven James"
-    - "Christopher Cleghorn"  
-    year: 2023
-    tags:
-     - neural architecture search
-    abstract: "Large Language Models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span numerous areas, and one area where they have made a significant impact is in the domain of code generation. In this context, we view LLMs as mutation and crossover tools. Meanwhile, Quality-Diversity (QD) algorithms are known to discover diverse and\nrobust solutions. By merging the code-generating abilities of LLMs with the diversity and robustness of QD solutions, we introduce LLMatic, a Neural Architecture Search (NAS) algorithm. While LLMs struggle to conduct NAS directly through prompts, LLMatic uses a procedural approach, leveraging QD for prompts and network architecture to create diverse and highly performant networks. We test LLMatic on the CIFAR-10 image classification benchmark, demonstrating that it can produce competitive networks with just $2,000$ searches, even without prior knowledge of the benchmark domain or exposure to any previous top-performing models for the benchmark."
-    pdfurl: "http://arxiv.org/pdf/2306.01102v3"
-    bibtex: |
-            "@article{nasir2023llmatic,
-              ttitle={LLMatic Neural Architecture Search via Large Language Models and Quality Diversity Optimization},
-              author={U. Nasir, Muhammad and Earle, Sam and Togelius, Julian and James, Steven and Cleghorn, Christopher},
-              journal={arXiv preprint arXiv:2306.01102},
-              year={2023}
-            }
-            "
+   - "Muhammad U. Nasir"
+   - "Sam Earle"
+   - "Julian Togelius"
+   - "Steven James"
+   - "Christopher Cleghorn"  
+  year: 2023
+  tags:
+    - neural architecture search
+  abstract: "Large Language Models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span numerous areas, and one area where they have made a significant impact is in the domain of code generation. In this context, we view LLMs as mutation and crossover tools. Meanwhile, Quality-Diversity (QD) algorithms are known to discover diverse and\nrobust solutions. By merging the code-generating abilities of LLMs with the diversity and robustness of QD solutions, we introduce LLMatic, a Neural Architecture Search (NAS) algorithm. While LLMs struggle to conduct NAS directly through prompts, LLMatic uses a procedural approach, leveraging QD for prompts and network architecture to create diverse and highly performant networks. We test LLMatic on the CIFAR-10 image classification benchmark, demonstrating that it can produce competitive networks with just $2,000$ searches, even without prior knowledge of the benchmark domain or exposure to any previous top-performing models for the benchmark."
+  pdfurl: "http://arxiv.org/pdf/2306.01102v3"
+  bibtex: |
+          "@article{nasir2023llmatic,
+            ttitle={LLMatic Neural Architecture Search via Large Language Models and Quality Diversity Optimization},
+            author={U. Nasir, Muhammad and Earle, Sam and Togelius, Julian and James, Steven and Cleghorn, Christopher},
+            journal={arXiv preprint arXiv:2306.01102},
+            year={2023}
+          }
+          "