Update README.md

README.md

@@ -215,70 +215,17 @@ Use the code below to get started with the model.
 
 #### Summary
 
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
+LLM-based Fine-grained Conditional Probability Estimation
 
 ## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
+```bibtex
+@article{wang2025always,
+  title={Always Tell Me The Odds: Fine-grained Conditional Probability Estimation},
+  author={Wang, Liaoyaqi and Jiang, Zhengping and Liu, Anqi and Van Durme, Benjamin},
+  journal={arXiv preprint arXiv:2505.01595},
+  year={2025}
+}
+```
 
-[More Information Needed]
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->