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The `ui_survey` dataset is a collection of datasets derived from unemployment insurance state survey. Specifically, it is derived from the Department of Labor's (DoL) [Comparison of State Unemployment Insurance Laws](https://oui.doleta.gov/unemploy/pdf/uilawcompar/2023/complete.pdf) which they publish annually. (This dataset is based off of the 2023 survey.) The dataset was designed for the benchmarking of both LLMs' retrieval and statutory reasoning ability.
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The first dataset is `ui_tables` which aims to store the information from the DoL's survey. `ui_tables` contains _82 rows_
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## Dataset Structure
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### Data Instance
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An instance of `ui_survey` (here, index 13) looks like
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```json
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1 OK Last 3 years Last 3 years
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```
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An instance of `ui_qas` looks like
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### Data Fields
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The `ui_survey` dataset is a collection of datasets derived from unemployment insurance state survey. Specifically, it is derived from the Department of Labor's (DoL) [Comparison of State Unemployment Insurance Laws](https://oui.doleta.gov/unemploy/pdf/uilawcompar/2023/complete.pdf) which they publish annually. (This dataset is based off of the 2023 survey.) The dataset was designed for the benchmarking of both LLMs' retrieval and statutory reasoning ability.
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The first dataset is `ui_tables` which aims to store the information from the DoL's survey. `ui_tables` contains _82 rows_ corresponding roughly to the .
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## Dataset Structure
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```python
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>>> load_dataset('ehariri/ui_survey', 'ui_tables')
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Dataset({
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features: ['table_id', 'table_name', 'table_description', 'notes', 'table_data', 'chapter', 'headers', 'prompt_context', 'prompts'],
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num_rows: 82
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})
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>>> load_dataset('ehariri/ui_survey', 'ui_qas')
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Dataset({
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features: ['idx', 'table_id', 'column_num', 'jurisdiction', 'question_context', 'question', 'answer'],
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num_rows: 3400
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})
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```
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### Data Instance
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An instance of `ui_survey` (here, index 13) looks like
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```json
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1 OK Last 3 years Last 3 years
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```
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An instance of `ui_qas` looks like
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```json
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{
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"idx": 0,
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"table_id": "1-1",
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"column_num": 0,
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"jurisdiction": "Alaska",
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"question_context": "EMPLOYERS As mentioned above, one of the basic factors in determining coverage is whether services are performed for employers. The coverage provisions of most state laws use the terms “employing unit” and “employer” to make the distinctions needed to address this issue. “Employing unit” is the more generic term, it applies to any one of several specified types of legal entities that has one or more individuals performing service for it within a state. An “employer” is an employing unit that meets the specific requirements of UI law. Accordingly, services provided for an “employer” are covered, and, as a result, an employer is subject to UI tax liability and its workers accrue rights to receive UI benefits. \nFor federal UI purposes, whether an employing unit is an employer depends on the number of days or weeks a worker is employed or the amount of the employing unit’s quarterly or yearly payroll. Except for agricultural labor and domestic service, FUTA applies to employing units who paid wages of $1,500 or more during any calendar quarter in the current or immediately preceding calendar year, or to employing units with one or more workers on at least one day in each of 20 different weeks during the current or immediately preceding calendar year. About half of the states use this federal definition. \nThe following table provides information on which employing units are considered employers in each state that uses a definition other than the one in FUTA.",
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"question": "Given the description above, what is either the minimum period of time or the payroll required for an employing unit to be considered an employer in Alaska?",
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"answer": "Any time"
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}
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```
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### Data Fields
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#### `ui_tables`
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* `table_id`: ID of table written as "[chapter no.]-[table no. within chapter]"
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* `table_name`: str, title of the table as noted on the DoL report
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* `table_description`: str, description of the table
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* `note`: str, any footnotes and other key information stored for this table
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* `table_data`: str, the data from this table as derived from the DoL report
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* `eval(...)` should produce a list of dictionaries corressponding to the table
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* `chapter`:
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* `header`:
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* `prompt_context`: str, context for the prompt derived
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* `prompt`: str, prompt
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#### `ui_qas`
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