Card: list paper/authors/code on separate lines; rename Related work

README.md

@@ -39,9 +39,9 @@ configs:
 
 # AbsVis: Benchmarking How Humans and Vision-Language Models "See" Abstract Concepts in Images
 
-**Paper:** [AbsVis – Benchmarking How Humans and Vision-Language Models "See" Abstract Concepts in Images](https://aclanthology.org/2025.emnlp-main.417/) (EMNLP 2025, Main Conference)
-**Authors:** Tarun Tater, Diego Frassinelli, Sabine Schulte im Walde
-**Code & raw CSVs:** [github.com/TarunTater/AbsVis_Emnlp2025](https://github.com/TarunTater/AbsVis_Emnlp2025)
+- **Paper:** [AbsVis – Benchmarking How Humans and Vision-Language Models "See" Abstract Concepts in Images](https://aclanthology.org/2025.emnlp-main.417/) (EMNLP 2025, Main Conference)
+- **Authors:** Tarun Tater, Diego Frassinelli, Sabine Schulte im Walde
+- **Code & raw CSVs:** [github.com/TarunTater/AbsVis_Emnlp2025](https://github.com/TarunTater/AbsVis_Emnlp2025)
 
 Abstract concepts like *mercy* and *peace* often lack clear visual grounding. AbsVis is a dataset of
 **675 images** annotated with **concept–explanation pairs** from humans and two vision-language models
@@ -160,7 +160,7 @@ The images come from YFCC100M; please also cite:
 }
 ```
 
-## Related work by the authors
+## Related work
 
 - Tater et al. (2024). [Unveiling the Mystery of Visual Attributes of Concrete and Abstract Concepts: Variability, Nearest Neighbors, and Challenging Categories](https://aclanthology.org/2024.emnlp-main.1203/). EMNLP 2024.
 - Tater et al. (2024). [Evaluating Semantic Relations in Predicting Textual Labels for Images of Abstract and Concrete Concepts](https://aclanthology.org/2024.cmcl-1.18/). CMCL 2024.