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@@ -118,18 +118,21 @@
           <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. In short, its <b>ecological footprint is dramatically lower</b>;</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.</li>
             <li>completely transparent in their functioning. There is also no question about
-            the fact that <b>they memorize training data patterns</b>.</li>
+              the fact that <b>they memorize training data patterns</b>.</li>
           </ul>
           <p>On the downside,</p>
           <ul>
+            <li>Not as great as current Transformer-based LLMs, but we have not trained
+              beyond data set sizes with orders of magnitudes above 100 million words.
+              Watch this space!</li>
             <li>Memory requirements during training are <b>heavy with large datasets</b> 
-              (>32 GB RAM with >100 million words);</li>
+              (more than 32 GB RAM with more than 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, <b>trading speed for accuracy</b>.</li>
+              slower approximations of <i>k</i>-NN classifiers, <b>trading speed for accuracy</b>.</li>
           </ul>
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       </div>