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@@ -100,7 +100,8 @@
         <h2 class="title is-3">Abstract</h2>
         <div class="content has-text-justified">
           <p>
-            WOPR, Word Predictor, is a memory-based language model developed in 2006-2011.
+            WOPR, Word Predictor, is a memory-based language model developed in 2006-2011,
+            and woken up from its cryogenic sleep in a better era.
           </p>
           <p>
             A memory-based language model, in this case running on the TiMBL classifier,
@@ -115,16 +116,18 @@
           <ul>
             <li>very efficient in training. Training is essentially reading the data (in linear time)
               and compressing it into a decision tree structure. This can be done on CPUs,
-                with sufficient RAM;</li>
+                with sufficient RAM. In short, its <b>ecological footprint is dramatically lower</b>;</li>
             <li>pretty efficient in generation when running with the fastest decision-tree
-              approximations of <i>k</i>-NN classification. This can be done on CPUs as well.
-              Accuracy is traded for speed, however.</li>
+              approximations of <i>k</i>-NN classification. This can be done on CPUs as well.</li>
+            <li>completely transparent in their functioning. There is also no question about
+            the fact that <b>they memorize training data patterns</b>.</li>
           </ul>
           <p>On the downside,</p>
           <ul>
-            <li>Memory requirements during training are heavy with large datasets (>100 million words);</li>
+            <li>Memory requirements during training are <b>heavy with large datasets</b> 
+              (>32 GB RAM with >100 million words);</li>
             <li>Memory-based LLMs are not efficient at generation time when running relatively
-            slower approximations of <i>k</i>-NN classifiers, trading speed for accuracy.</li>
+            slower approximations of <i>k</i>-NN classifiers, <b>trading speed for accuracy</b>.</li>
           </ul>
         </div>
       </div>