add link to pre-print

README.md

@@ -29,13 +29,13 @@ size_categories:
 # TableEval dataset
 
 [![GitHub](https://img.shields.io/badge/GitHub-000000?style=flat&logo=github&logoColor=white)](https://github.com/esborisova/TableEval-Study)
-[![PDF](https://img.shields.io/badge/PDF-red)](https://openreview.net/pdf?id=umbMEwiTtq)
+[![arXiv](https://img.shields.io/badge/arXiv-darkred)](https://arxiv.org/abs/2507.00152)
 
 **TableEval** is developed to benchmark and compare the performance of (M)LLMs on tables from scientific vs. non-scientific sources, represented as images vs. text. 
 It comprises six data subsets derived from the test sets of existing benchmarks for question answering (QA) and table-to-text (T2T) tasks, containing a total of **3017 tables** and **11312 instances**. 
 The scienfific subset includes tables from pre-prints and peer-reviewed scholarly publications, while the non-scientific subset involves tables from Wikipedia and financial reports. 
 Each table is available as a **PNG** image and in four textual formats: **HTML**, **XML**, **LaTeX**, and **Dictionary (Dict)**. 
-All task annotations are taken from the source datasets. Please, refer to the [Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data](https://openreview.net/pdf?id=umbMEwiTtq) paper for more details.
+All task annotations are taken from the source datasets. Please, refer to the [Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data](https://arxiv.org/abs/2507.00152) paper for more details.
 
 
 ## Overview and statistics