Update README.md

README.md

@@ -12,7 +12,7 @@ size_categories:
 <!-- Provide a quick summary of the dataset. -->
 
 <!-- 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).-->
-This dataset consists of three novel reasoning datasets [Links here]Each dataset contains a response prompt (a
+This dataset consists of three novel reasoning datasets.  Each dataset contains a response prompt (a
 question to ask a user or an LLM) and a ground truth answer. 
 
 ## Dataset Details
@@ -23,6 +23,70 @@ The factoid evaluates the ability to retrieve information when there might not b
 ### Dataset Description
 
 <!-- Provide a longer summary of what this dataset is. -->
+-   **Dataset 1: Arithmetic (Counting)**:
+-   This dataset tests the ability to count days in a particular country
+with multiple arrival and departure dates. The ground truth answer is
+a single integer number. The dataset is generated for a set of 100
+arrival and departure dates.
+-   **Dataset 2: Multihop (RAGs)**: This dataset tests the
+ability to perform information retrieval to determine if there are
+24/7 CVS pharmacies in a specified city. The ground truth answer is a
+single integer number indicating the number of 24-hour pharmacies,
+which could be 0. The dataset is generated for the 387 cities in
+California with a CVS store.  
+-   **Dataset 3: Factoid**:  This
+dataset tests the ability to retrieve facts where there may not be an
+answer. This dataset asks about the national sport for a specified
+country, which may not exist. The ground truth answer is a string
+indicating the national sport or "Nothing" if the national sport does
+not exist. This dataset is generated for the 249 countries in the
+world.
+
+
+- **Curated by:** Nikhil Wani and Leilani Gilpin
+- **Funded by:** Funding for this work was provided by Underwriters Laboratories Inc. through the Center for Advancing Safety of Machine Intelligence.
+<!-- - **Shared by [optional]:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed] --> 
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+<!-- - **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed] -->
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+This dataset is designed to evaluate AI models across three key capabilities: arithmetic reasoning, multistep information retrieval, and fact-based knowledge queries. The arithmetic dataset involves calculating the total days spent in the U.S. based on simulated travel itineraries, testing logical date-based reasoning. The multihop dataset assesses a model’s ability to retrieve and aggregate information, such as identifying 24/7 CVS pharmacies in California cities through multiple steps. The factoid dataset evaluates the ability to retrieve factual information, including handling cases where no definitive answer exists, like determining a country's national sport. These datasets collectively serve as a benchmark for testing and improving AI systems’ reasoning, retrieval, and knowledge-handling skills.
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+This dataset is suited for benchmarking and improving AI systems in
+arithmetic reasoning, multistep information retrieval, and fact-based
+knowledge tasks. It can be used to train or evaluate models for
+the ability to handle uncertainty in tasks. It supports the development of retrieval-augmented systems and
+knowledge-based applications, including trivia systems and tools that
+handle "null answer" scenarios. 
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+This dataset is not suitable for tasks outside its specific domains of
+arithmetic reasoning, information retrieval, and fact-based question
+answering. It unsuitable for training or evaluating models
+on open-domain reasoning, ethical decision-making, or multimodal
+applications involving images or audio. 
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+The dataset consists of two columns, the prompt and the true answer.  
+
+## Dataset Creation
 -   **Dataset 1: Arithmetic (Counting)**: This dataset tests the ability
     to count days in a particular country with multiple arrival and
     departure dates. The ground truth answer is a single integer number
@@ -67,108 +131,84 @@ The factoid evaluates the ability to retrieve information when there might not b
     (if it exists).  For countries without a designated national sport,
     the value is set to "Nothing."
 
-
-
-
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-
-### Direct Use
-
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
-
-## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
-
-## Dataset Creation
-
 ### Curation Rationale
 
 <!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+The curation rationale for this dataset is centered on creating
+targeted benchmarks to evaluate LLMs on uncertain tasks.Each dataset
+is designed to simulate realistic scenarios: the arithmetic dataset
+emphasizes logical reasoning with date-based calculations, reflecting
+common tasks like travel planning. The multihop dataset requires
+retrieving and aggregating structured information across multiple
+steps. The factoid dataset tests knowledge retrieval and handling of
+incomplete information, such as cases where answers may not exist. 
 
 ### Source Data
-
+The source data comes from Wikipedia, and the CVS website.  
 <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
 
 #### Data Collection and Processing
-
+Beautiful Soup was used to scrape information from the source data.
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
 
-[More Information Needed]
 
-#### Who are the source data producers?
+#### Who are the source data producers? 
+The source data for this dataset comes from publicly available
+resources and synthetic generation. The arithmetic dataset uses
+randomized date logic without external data. The multihop dataset
+relies on CVS's official store locator and pharmacy location pages. The
+factoid dataset draws from the `pycountry.countries` Python library for
+a list of countries and Wikipedia's national sports page for factual
+information. 
 
 <!-- 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. -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-### Annotations [optional]
+<!-- ### Annotations [optional]
 
 <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
 
-#### Annotation process
+<!-- #### 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. -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-#### Who are the annotators?
+<!-- #### Who are the annotators?
 
 <!-- This section describes the people or systems who created the annotations. -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
 #### Personal and Sensitive Information
+There is no personal nor 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]
-
 ## Bias, Risks, and Limitations
-
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+Since the data is synthetically generated or scraped from specific
+online sources, it may not fully capture the diversity of real-world
+scenarios. For example, the arithmetic dataset's travel itineraries
+are based on random date generation, which may not reflect real-world
+travel patterns or demographic factors that could affect travel
+behavior. The multihop dataset is centered on CVS stores in California,
+may introduce regional bia. The factoid dataset relies on Wikipedia for
+national sports data, which may be incomplete or biased, as not all
+countries have a nationally recognized sport. 
+
+<!-- ### 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.
+<!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. -->
 
-## Citation [optional]
+<!-- ## Citation [optional] -->
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
+<!-- **BibTeX:**
 
 [More Information Needed]
 
@@ -176,11 +216,11 @@ Users should be made aware of the risks, biases and limitations of the dataset.
 
 [More Information Needed]
 
-## Glossary [optional]
+## Glossary [optional] -->
 
 <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
 
-[More Information Needed]
+<!-- [More Information Needed]
 
 ## More Information [optional]
 
@@ -192,4 +232,4 @@ Users should be made aware of the risks, biases and limitations of the dataset.
 
 ## Dataset Card Contact
 
-[More Information Needed]
+[More Information Needed] -->