Intro clarification

_README.md

@@ -5,7 +5,7 @@ pairs of responses to a judge who determines which response better
 addresses the prompt's intention. Rather than compare all response
 pairs, the framework sets one model as a baseline, then individually
 compares all responses to that. Its primary method of ranking models
-via win percentages over the baseline.
+is with win percentages over the baseline.
 
 This Space presents an alternative method of ranking based on the
 [Bradley–Terry
@@ -20,12 +20,12 @@ then be used to predict outcomes between teams that have yet to play.
 
 The Alpaca project presents a good opportunity to apply BT in
 practice; especially since BT fits nicely into a Bayesian analysis
-framework. As LLMs become more pervasive, quantifying uncertainty in
-their evaluation is increasingly important; something that Bayesian
+framework. As LLMs become more pervasive, so to is considering
+evaluation uncertainty when comparing them; something that Bayesian
 frameworks do well.
 
 This Space is divided into two primary sections: the first presents a
 ranking of models based on estimated ability. The figure on the right
 visualizes this ranking for the top 10 models, while the table below
-it presents the full set. The second section estimates the probability
+presents the full set. The second section estimates the probability
 that one model will be preferred to another.