description

src/display/about.py

@@ -28,9 +28,7 @@ TITLE = """<h1 align="center" id="space-title"><code>/\/</code> &nbsp; Open CoT
 INTRODUCTION_TEXT = """
 The `/\/` Open CoT Leaderboard tracks the reasoning skills of LLMs, measured as their ability to generate **effective chain-of-thought reasoning traces**.
 
-The leaderboard reports **accuracy gains** achieved by using CoT, i.e.
-
-> _accuracy gain Δ_ =  _CoT accuracy_ - _baseline accuracy_.
+The leaderboard reports **accuracy gains** achieved by using CoT, i.e.: _accuracy gain Δ_ = _CoT accuracy_ – _baseline accuracy_.
 
 See the "About" tab for more details and motivation.
 """
@@ -39,14 +37,14 @@ See the "About" tab for more details and motivation.
 LLM_BENCHMARKS_TEXT = """
 ## How it works
 
-A CoT `regime` consists in a prompt chain and decoding parameters used to generate a reasoning trace. To assess the reasoning skill of a given `model`, we carry out the following steps for each `task` (test dataset) and each `regime`:
+To assess the reasoning skill of a given `model`, we carry out the following steps for each `task` (test dataset) and different CoT `regimes`. (A CoT `regime` consists in a prompt chain and decoding parameters used to generate a reasoning trace.)
 
-1. Generate CoT reasoning traces for all problems in the test dataset with `model` and according to `regime`.
-2. Let the model answer the test dataset problems and record the resulting _baseline accuracy_.
-3. Let the model answer the test dataset problems _with the reasoning traces appended_ to the prompt and record the resulting _CoT accuracy_.
-4. Compute the _accuracy gain Δ_ =  _CoT accuracy_ - _baseline accuracy_ for the given `model`, `task`, and `regime`.
+1. Let the `model` generate CoT reasoning traces for all problems in the test dataset according to `regime`.
+2. Let the `model` answer the test dataset problems, and record the resulting _baseline accuracy_.
+3. Let the `model` answer the test dataset problems _with the reasoning traces appended_ to the prompt, and record the resulting _CoT accuracy_.
+4. Compute the _accuracy gain Δ_ = _CoT accuracy_ – _baseline accuracy_ for the given `model`, `task`, and `regime`.
 
-Each regime has a different accuracy gain Δ, and the leaderboard reports the best Δ achieved by a regime.
+Each `regime` has a different accuracy gain Δ, and the leaderboard reports the best Δ achieved by any regime.
 
 
 ## How is it different from other leaderboards?