Update README.md

README.md

@@ -241,7 +241,6 @@ Please cite this data using:
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2024.acl-long.261/",
     doi = "10.18653/v1/2024.acl-long.261",
-    pages = "4750--4767",
-    abstract = "Spatial reasoning is a crucial component of both biological and artificial intelligence. In this work, we present a comprehensive study of the capability of current state-of-the-art large language models (LLMs) on spatial reasoning. To support our study, we created and contribute a novel Spatial Reasoning Characterization (SpaRC) framework and Spatial Reasoning Paths (SpaRP) datasets, to enable an in-depth understanding of the spatial relations and compositions as well as the usefulness of spatial reasoning chains. We found that all the state-of-the-art LLMs do not perform well on the datasets{---}their performances are consistently low across different setups. The spatial reasoning capability improves substantially as model sizes scale up. Finetuning both large language models (e.g., Llama-2-70B) and smaller ones (e.g., Llama-2-13B) can significantly improve their F1-scores by 7{--}32 absolute points. We also found that the top proprietary LLMs still significantly outperform their open-source counterparts in topological spatial understanding and reasoning."
+    pages = "4750--4767"
 }
 ```