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@@ -131,19 +131,19 @@ <h2 class="subtitle has-text-centered">
                     <h2 class="title is-3">Abstract</h2>
                     <div class="content has-text-justified">
                         <p>
-                            We introduce a Large Language Model (LLM) framework that generates rich and diverse NL
-                            datasets using only Vega-Lite specifications as input, thereby streamlining the development
-                            of Natural Language Interfaces (NLIs) for data visualization. We propose two techniques to
-                            synthesize relevant chart semantics accurately and enhance syntactic diversity in each NL
-                            dataset, respectively: 1) a guided discovery incorporated into prompting so that LLMs can
-                            steer themselves to create varying NL datasets in a self-directed manner; 2) a score-based
-                            paraphrasing to augment NL syntax along with four well-defined language axes. We also
-                            present a new chart collection of 1,981 real-world Vega-Lite specifications that have
-                            increased diversity and complexity compared to benchmarks, to demonstrate the
-                            generalizability of our framework. The experimental results show that our framework
-                            accurately extracts chart semantics and generates L1/L2 captions with 89.4% and 76.0%
-                            accuracy, respectively, while generating and paraphrasing utterances and questions with
-                            greater diversity than benchmarks.
+                            We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse
+                            NL datasets using only Vega-Lite specifications as input, thereby streamlining the
+                            development of Natural Language Interfaces (NLIs) for data visualization. To synthesize
+                            relevant chart semantics accurately and enhance syntactic diversity in each NL dataset, we
+                            leverage 1) a guided discovery incorporated into prompting so that LLMs can steer themselves
+                            to create faithful NL datasets in a self-directed manner; 2) a score-based paraphrasing to
+                            augment NL syntax along with four language axes. We also present a new collection of 1,981
+                            real-world Vega-Lite specifications that have increased diversity and complexity than
+                            existing chart collections. When tested on our chart collection, VL2NL extracted chart
+                            semantics and generated L1/L2 captions with 89.4% and 76.0% accuracy, respectively. It also
+                            demonstrated generating and paraphrasing utterances and questions with greater diversity
+                            compared to the benchmarks. Last, we discuss how our NL datasets and framework can be
+                            utilized in real-world scenarios.
                         </p>
                     </div>
                 </div>