Update README.md

README.md

@@ -54,7 +54,7 @@ The dataset targets **low-resource data-to-text (D2T)** generation where models
 - **Input:** a drone **status** dictionary (e.g., wind speed, battery level, altitude, pilot experience, etc.) and a time-ordered list of **time-step objects** near the flight path (type, distance, moving/in-path flags, timestamps).
 - **Output:** a **handover message** (English) that surfaces only *critical* information (e.g., “Risk of physical damage! There is a castle in the drone’s flight path at a distance of 2.5 m.”)
 
-The RAMP paper reports a **low-resource** setup with ~**1.6K data points** (input–output pairs). Inputs average **~541 tokens** (range ~274–2481), and outputs average **~149 tokens** (range ~29–1263), reflecting long, information-dense inputs common in real-time settings. The dataset is organized to support **retrieval-augmented** few-shot prompting and **modular prompt-tuning**.
+The RAMP paper reports a **low-resource** setup with **1.6K data points** (input–output pairs). Inputs average **541 tokens** (range 274–2481), and outputs average **149 tokens** (range 29–1263), reflecting long, information-dense inputs common in real-time settings. The dataset is organized to support **retrieval-augmented** few-shot prompting and **modular prompt-tuning**.
 
 - **Curated by:** Ruitao Feng, Xudong Hong, Mayank Jobanputra, Mattes Warning, Vera Demberg  
 - **Language(s) (NLP):** English