Update README.md

README.md

@@ -18,14 +18,10 @@ language:
 
 This dataset is the Version 2.0 of [`microsoft/FStarDataSet`](https://huggingface.co/datasets/microsoft/FStarDataSet).
 
-## Data Source
-In addition to the eight projects in `microsoft/FStarDataSet`, data from four more projects are included in this version. 
-1. [Starmada](https://github.com/microsoft/Armada):  a framework for doing proofs by stepwise refinement for concurrent programs in a weak memory model. Starmada is an experimental version of Armada implemented in F⋆, relying on various advanced features of F⋆’s dependent type system for more generic and abstract proofs.
-2. [Zeta](https://github.com/project-everest/zeta): a high performance, concurrent monitor for stateful services proven correct in F⋆ and its Steel concurrent separation logic
-3. [Dice-star](https://github.com/verified-HRoT/dice-star):  a verified implementation of the DICE measured boot protocol for embedded devices
-4. [Noise-star](https://github.com/Inria-Prosecco/noise-star): a verified compiler for implementations of Noise protocols, a family of key-exchange protocols
 
 ## Primary-Objective
+This dataset's primary objective is to train and evaluate Proof-oriented Programming with AI (PoPAI, in short). Given a specification of a program and proof is F*, 
+the objective of a AI model is to synthesize the implemantation (see [below](#usage) for details about the usage of this dataset, including the input and output). 
 
 ## Data Format
 Each of the examples in this dataset are organized as dictionaries with the following schema
@@ -61,10 +57,29 @@ Each of the examples in this dataset are organized as dictionaries with the foll
 ```
 
 # Usage
+To use this dataset with [`datasets`](https://pypi.org/project/datasets/), 
+```python
+from datasets import load_dataset
+
+data = load_dataset("microsoft/FStarDataSet")
+train_data = data["train"]
+eval_data = data["validation"]
+test_data = data["test"]
+
+intra_project_test = test_data.filter(lambda x: x["isa_cross_project_example"] == False)
+cross_project_test = test_data.filter(lambda x: x["isa_cross_project_example"] == True)
+```
 
 ## Input
+The primary input for generating F* definition is **`source_type`**. 
+All other information in an example may be used directly or to derive an input except 
+**`source_definition`**, **`ideal_premises`**, and **`completed_definiton`**.
+
 
 ## Output
+The primary output is **`source_definition`**, which is the ground truth definition, that can be evaluated with the [proof checker](#evaluation-on-this-dataset). 
+The **`completed_definiton`** may be used as ground truth when a model is used as a text completion setting (though the evaluator does not support evaluation in this setting). 
+In addition, **`ideal_premises`** may be used for evaluating premise selection models. 
 
 # Evaluation on this dataset
 Generated F* definitions should be evaluated the proof checker tool from 
@@ -75,4 +90,25 @@ Download the source code and the `helpers.zip` file from the release.
 The attached binaries in the evaluator (i.e., `fstar.exe` and `z3`) are built on 
 **`Ubuntu 20.04.6 LTS (GNU/Linux 5.4.0-189-generic x86_64)`**,  **`gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2)`**,  **`OCaml 4.12.0`**. 
 If any of the binaries do not work properly, build F* from [this commit (10183ea187da8e8c426b799df6c825e24c0767d3)](https://github.com/FStarLang/FStar/commit/10183ea187da8e8c426b799df6c825e24c0767d3) 
-from the [F* repository](https://github.com/FStarLang/FStar), using their [installation guide](https://github.com/FStarLang/FStar/blob/master/INSTALL.md).
+from the [F* repository](https://github.com/FStarLang/FStar), using their [installation guide](https://github.com/FStarLang/FStar/blob/master/INSTALL.md).
+
+# Data Source
+In addition to the eight projects in `microsoft/FStarDataSet`, data from four more projects are included in this version. 
+1. [Starmada](https://github.com/microsoft/Armada):  a framework for doing proofs by stepwise refinement for concurrent programs in a weak memory model. Starmada is an experimental version of Armada implemented in F⋆, relying on various advanced features of F⋆’s dependent type system for more generic and abstract proofs.
+2. [Zeta](https://github.com/project-everest/zeta): a high performance, concurrent monitor for stateful services proven correct in F⋆ and its Steel concurrent separation logic
+3. [Dice-star](https://github.com/verified-HRoT/dice-star):  a verified implementation of the DICE measured boot protocol for embedded devices
+4. [Noise-star](https://github.com/Inria-Prosecco/noise-star): a verified compiler for implementations of Noise protocols, a family of key-exchange protocols
+
+# Limitations
+**TDB**
+
+# Citation
+```
+@inproceedings{chakraborty2024towards,
+  title={Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming},
+  author={Chakraborty, Saikat and Ebner, Gabriel and Bhat, Siddharth and Fakhoury, Sarah and Fatima, Sakina and Lahiri, Shuvendu and Swamy, Nikhil},
+  booktitle={Proceedings of the IEEE/ACM 47th International Conference on Software Engineering (To Appear)},
+  pages={1--12},
+  year={2025}
+}
+```