Update README.md

README.md

@@ -12,12 +12,9 @@ language:
 
 # Model Card for ValueLlama
 
-<!-- Provide a quick summary of what the model is/does. -->
-
 
 ## Model Description
 
-<!-- Provide a longer summary of what this model is. -->
 
 ValueLlama is designed for perception-level value measurement in an open-ended value space, which includes two tasks: (1) Relevance classification determines whether a perception is relevant to a value; and (2) Valence classification determines whether a perception supports, opposes, or remains neutral (context-dependent) towards a value. Both tasks are formulated as generating a label given a value and a perception.
 
@@ -27,9 +24,8 @@ ValueLlama is designed for perception-level value measurement in an open-ended v
 
 ## Paper
 
-<!-- Provide the basic links for the model. -->
 
-For more information, please refer to our paper: [TODO]().
+For more information, please refer to our paper: [*Measuring Human and AI Values based on Generative Psychometrics with Large Language Models*](https://arxiv.org/abs/2409.12106).
 
 ## Uses
 
@@ -40,5 +36,17 @@ See our codebase for more details: [https://github.com/Value4AI/gpv](https://git
 
 ## BibTeX:
 
-[More Information Needed]
+If you find this model helpful, we would appreciate it if you cite our paper:
+
+```bibtex
+@misc{ye2024gpv,
+      title={Measuring Human and AI Values based on Generative Psychometrics with Large Language Models}, 
+      author={Haoran Ye and Yuhang Xie and Yuanyi Ren and Hanjun Fang and Xin Zhang and Guojie Song},
+      year={2024},
+      eprint={2409.12106},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2409.12106}, 
+}
+```