Update 2024-05-30-counting.md

PolymathicAI · Jun 16, 2024 · 4bbd1e9 · 4bbd1e9
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@@ -50,8 +50,8 @@ This information helps disambiguate the different regions based on context.
 
 #### Key Propositions
 
-1. **Proposition 1:** If the regional contextual position information is available in the latent representation of the tokens at some layer of a Transformer, the contextual counting task can be solved with a single additional layer.
-2. **Proposition 2:** A causal Transformer with a single layer and no position encoding (NoPE) can infer the regional contextual position.
+- **Proposition 1:** If the regional contextual position information is available in the latent representation of the tokens at some layer of a Transformer, the contextual counting task can be solved with a single additional layer.
+- **Proposition 2:** A causal Transformer with a single layer and no position encoding (NoPE) can infer the regional contextual position.
 
 These propositions imply that a two-layer causal Transformer with NoPE can solve the contextual counting task.