Update ReadMe

README.md

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 SimCoPilot is a benchmark for evaluating LLMs to perform as a "copilot"-style, interactive coding assistant.
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
-
 ## Dataset Details
 
 ### Dataset Description
 
 SimCoPilot is a benchmark for evaluating LLMs to perform as a "copilot"-style, interactive coding assistant, testing their ability to add and complete code in complex real-world software environments and analyzing how LLMs manage different code dependencies and logic complexities.
 
-
-
 - **Curated by:** Mingchao Jiang
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
@@ -32,7 +28,7 @@ SimCoPilot is a benchmark for evaluating LLMs to perform as a "copilot"-style, i
 
 ### Dataset Sources [optional]
 
-<!-- Provide the basic links for the dataset. -->
+The source code and supporting material can be found in the Github link below
 
 - **Repository:** https://github.com/mj33rice/SimCoPilot
 - **Paper [optional]:** [More Information Needed]
@@ -62,25 +58,26 @@ Commercial Purposes, Infer Personal Information
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
+Currently, the most widely-used benchmarks for checking the ability of AI models to perform program synthesis (``AI-for-code'') consist of a detailed English description of a concise, self-contained code to synthesize, as well as a few test cases to test the correctness of the synthesized code. 
+While such benchmarks are useful, they match one particularly narrow use case, where the goal is to synthesize a relatively short, complete, standalone program.
 
-[More Information Needed]
+We introduce SimCoPilot, a novel benchmark crafted to simulate the ability of an AI such as a large language model (LLM) to perform as a ``copilot''-style, interactive coding assistant.
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+Source Code
 
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+Emails were sent to faculty and students within the Rice University Computer Science, Electrical Engineering, and Statistics departments, inviting them to contribute Java and Python code private repositories for AI-for-code research. 
+Upon receipt, 1,163 code generation tasks were curated to ensure a diverse and representative sample of real-world code, gathering approximately 11,000 lines of code.
 
-[More Information Needed]
 
-#### Who are the source data producers?
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+#### Who are the source data producers?
 
-[More Information Needed]
+The dataset includes Java and Python code contributions primarily from students and faculty at Rice University's Computer Science department, Electrical Engineering, and Statistics departments, 
+representing a community of academic programmers and developers.
 
 ### Annotations [optional]
 
@@ -88,33 +85,34 @@ Commercial Purposes, Infer Personal Information
 
 #### Annotation process
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+Each of the 1,163 programming tasks was created from eight Java repositories and seven Python repositories, totaling nearly 11,000 lines of code.
 
-[More Information Needed]
+Our team went through these codes, generating both infill and completion tasks.  
+To create an infill task, the annotator picks a meaningful starting point for the AI-for-code model to begin writing code (at the beginning of the boolean if condition, or at the beginning of the body of a for loop, for example) 
+and then marks the rest of that particular code block for deletion, to be re-created by the AI-for-code model.  
 
-#### Who are the annotators?
+In the case of an if condition, the entire boolean predicate would be marked for deletion.  
+In the case of a for-loop body, the entire body would be marked.  
 
-<!-- This section describes the people or systems who created the annotations. -->
+A completion task is created in much the same way, but the code for the remainder of the method or function is marked for deletion. 
 
-[More Information Needed]
+#### Who are the annotators?
+
+A team of three graduate students from Rice Univerity with 5-10 years of programming experience. 
 
 #### Personal and Sensitive Information
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
+N/A
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+Sample Bias: Contributions mainly from students and faculty at a single institution (Rice University) could reflect a biased sample of coding styles, proficiency levels, and problem-solving approaches. 
+Overfitting Risks: Models trained on this dataset might perform well on similar academic or controlled environments but may not generalize well to diverse coding tasks outside of these parameters.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+Diversifying the Data Sources: Expand the dataset to include code from a broader range of contributors beyond the academic circle of Rice University. This could involve soliciting code from developers in different industries, countries, and cultural backgrounds to enhance the dataset's diversity and representativeness. 
+Cross-Validation with External Datasets: Use external datasets for cross-validation of the AI models trained with this dataset. This helps in assessing the model’s performance and generalizability to other coding environments and tasks.
 
 ## Citation [optional]