Update src/about.py

src/about.py

@@ -72,8 +72,8 @@ We use the given *test* subset (for those benchmarks that also have *train* and
 These benchmarks are picked for now, but several other benchmarks are going to be added later to help us perform a more thorough examination of models.
 
 The last two benchmarks, ParsiNLU NLI and ParsiNLU QQP are evaluated in different few-shot settings and then the maximum score is returned as the final evaluation.
-We argue that this is indeed a fair evaluation scheme since many light-weight models (around ~7B and less) can have a poor in-context learning and thus perform better
-in small shots (or have a small knowledge capacity and perform poorly in zero-shot). We wish to not hold this against the model by trying to measure performances in different settings and take the maximum score achieved .
+We argue that this is indeed a fair evaluation scheme since many light-weight models (around ~7B and less) can have a poor in-context learning in long-context prompts and thus perform better
+in smaller shots (or have a small knowledge capacity and perform poorly in zero-shot). We wish to not hold this against the model by trying to measure performances in different settings and take the maximum score achieved .
 
 ## REPRODUCIBILITY
 The parameters used for evaluation along with instructions and prompts will be available once the framework is released. (TO BE COMPLETED)