Update index.md

Author: mtian8

docs/index.md

@@ -74,7 +74,7 @@ SciCode is a challenging benchmark designed to evaluate the capabilities of lang
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 SciCode sources challenging and realistic research-level coding problems across 6 natural science disciplines, covering a total of 16 subfields. This diverse selection ensures a comprehensive representation of the natural sciences, where extensive code development is essential. SciCode is mainly drawn from the scripts that scientists use in their everyday workflow. Many of these have been used in one or more publications, demonstrating their robustness and correctness.
 
-Among various coding necessities, Scicode mainly focuses on: 1. Numerical methods 2. Simulation of systems 3. Scientific calculation. These are the tasks we believe require intense scientific knowledge and reasoning to optimally test LM’s science capability. The below figure is an example of the combination of 1 and 3.
+Among various coding necessities, Scicode mainly focuses on: 1. Numerical methods. 2. Simulation of systems. 3. Scientific calculation. These are the tasks we believe require intense scientific knowledge and reasoning to optimally test LM’s science capability. The below figure is an example of the combination of 1 and 3.
 
 In designing test cases for evaluation, we incorporate domain-specific test cases in addition to numerical cases. These tests are extracted from real scientific workflows: scientists must design domain-specific test cases to verify code accuracy by reproducing results published in papers or matching analytical solutions derived from theoretical models. Each problem goes through <b>3</b> rounds of validation (i.e. by in-domain scientists, out-of-domain scientists, GPT4) for quality control.
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