Update README.md

README.md

@@ -1,9 +1,11 @@
-# LiveCLKTBench Example
-Domain: sports; Entity Date range:2026-03-20 ~ 2026-04-10 
-Domain: movie; Entity Date range:2026-01-01 ~ 2026-04-10 
+# LiveCLKTBench 
 
+Generation Config:
+- Domain: sports;    Entity Date range:2026-03-20 ~ 2026-04-10 
+- Domain: movie;     Entity Date range:2026-01-01 ~ 2026-04-10 
 
-## Overview [![arXiv](https://img.shields.io/badge/arXiv-2511.14774-b31b1b.svg)](https://arxiv.org/abs/2511.14774)
+
+## Overview 
 Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior exposure during pre-training.
 We present **LiveCLKTBench**, an automated pipeline for generating realistic, contamination-free, and continuously refreshable benchmarks for **C**ross-**L**ingual **K**nowledge **T**ransfer. 
 **LiveCLKTBench** identifies independent, time-sensitive knowledge entities from real-world domains, filters them based on temporal constraints, and verifies whether the model already possesses this knowledge. The documents corresponding to valid entities are then used to generate factual questions, which are translated into multiple languages to evaluate cross-lingual transfer.