Update index.html

index.html

@@ -625,12 +625,12 @@ <h2 class="title is-3">In-domain Evaluation</h2>
               </div>
               <div>
               <br>
-              Specifically, we observed these following takeaways:
+              Specifically, we observe the following takeaways:
                 <ol>
                   <li>By simply fine-tuning a large language model on TableInstruct, TableLlama can achieve comparable or even better performance on almost all the tasks <b>without any table pretraining or special table model architecture design</b>;</li>
-                  <li><b>TableLlama displays advantanges in table QA tasks</b>: <b>TableLlama</b> can surpass the SOTA by <b>5.61 points</b> for highlighted cell based table QA task (i.e., FeTaQA) and <b>17.71 points</b> for hierarchical table QA (i.e., HiTab), which is full of numerical reasoning on tables. As LLMs have shown superior in interacting with humans and answering questions, this indicates that <b>the existing underlying strong language understanding ability of LLMs may be beneficial for such table QA tasks, despite with semi-structured tables</b>;</li>
+                  <li><b>TableLlama displays advantages in table QA tasks</b>: <b>TableLlama</b> can surpass the SOTA by <b>5.61 points</b> for highlighted cell based table QA task (i.e., FeTaQA) and <b>17.71 points</b> for hierarchical table QA (i.e., HiTab), which is full of numerical reasoning on tables. As LLMs have been shown superior in interacting with humans and answering questions, this indicates that <b>the existing underlying strong language understanding ability of LLMs may be beneficial for such table QA tasks, despite with semi-structured tables</b>;</li>
                   <li><b>For the entity linking task</b>, which requires the model to link the mention in a table cell to the correct referent entity in Wikidata, <b>TableLlama</b> <b>also presents superior performance with 8 points gain over the SOTA performance</b>. Since the candidates are composed of their referent entity name and description, we hypothesize LLMs have certain abilities to understand the description which help identify the correct entities;</li>
-                  <li>Row population is the only task where <b>TableLlama</b> has a large performance gap compared to the SOTA. We observed that, <b>in order to correctly populate the entities from the given large number of candidates, the model needs to fully understand the inherent relation between the enquiried entity and each given candidate, which is still challenging for the current model</b>. Detailed analysis and case study can be found in our paper's <b>Section 4.1</b> and <b>Table 5 in Appendix A</b>.</li>
+                  <li>Row population is the only task where <b>TableLlama</b> has a large performance gap compared to the SOTA. We observe that, <b>in order to correctly populate the entities from the given large number of candidates, the model needs to fully understand the inherent relation between the enquiried entity and each given candidate, which is still challenging for the current model</b>. Detailed analysis and case study can be found in our paper's <b>Section 4.1</b> and <b>Table 5 in Appendix A</b>.</li>
                 </ol>
               </div>
             </div>