📄 Abercrombie¶

LegalBench is a collection of benchmark tasks for evaluating legal reasoning in large language models.

Tags: arxiv:2308.11462, croissant:True, finance:True, language:en, law:True, legal:True, license:other, region:us, size_categories:10K<n<100K, task_categories:['text-classification', 'question-answering', 'text-generation']

Note

ID: cards.legalbench.abercrombie | Type: TaskCard

{
    "__description__": "LegalBench is a collection of benchmark tasks for evaluating legal reasoning in large language models.",
    "__tags__": {
        "arxiv": "2308.11462",
        "croissant": true,
        "finance": true,
        "language": "en",
        "law": true,
        "legal": true,
        "license": "other",
        "region": "us",
        "size_categories": "10K<n<100K",
        "task_categories": [
            "text-classification",
            "question-answering",
            "text-generation"
        ]
    },
    "loader": {
        "name": "abercrombie",
        "path": "nguha/legalbench",
        "type": "load_hf"
    },
    "preprocess_steps": [
        {
            "field_to_field": {
                "answer": "label",
                "text": "text"
            },
            "type": "rename_fields"
        },
        {
            "fields": {
                "classes": [
                    "generic",
                    "descriptive",
                    "suggestive",
                    "arbitrary",
                    "fanciful"
                ],
                "classes_descriptions": "A mark is generic if it is the common name for the product. A mark is descriptive if it describes a purpose, nature, or attribute of the product. A mark is suggestive if it suggests or implies a quality or characteristic of the product. A mark is arbitrary if it is a real English word that has no relation to the product. A mark is fanciful if it is an invented word.",
                "text_type": "products",
                "type_of_class": "type of mark"
            },
            "type": "add_fields"
        }
    ],
    "task": "tasks.classification.multi_class.with_classes_descriptions",
    "templates": {
        "default": {
            "input_format": "Q: {text} What is the {type_of_class}?",
            "instruction": "{classes_descriptions}\n\nLabel the {type_of_class} for the following {text_type}:\n",
            "output_format": "{label}",
            "postprocessors": [
                "processors.take_first_non_empty_line",
                "processors.lower_case_till_punc"
            ],
            "target_prefix": "A: ",
            "title_fields": [],
            "type": "input_output_template"
        }
    },
    "type": "task_card"
}

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into a model-input, and specifies the metrics for evaluation of model-output.

Attributes:
loader: specifies the source address and the loading operator that can access that source and transform it into a unitxt multistream.

preprocess_steps: list of unitxt operators to process the data source into a model-input.

task: specifies the fields (of the already (pre)processed instance) making the inputs, the fields making the outputs, and the metrics to be used for evaluating the model output.

templates: format strings to be applied on the input fields (specified by the task) and the output fields. The template also carries the instructions and the list of postprocessing steps, to be applied to the model output.

Explanation about AddFields¶

Adds specified fields to each instance in a given stream or all streams (default) If fields exist, updates them.

Args:

fields (Dict[str, object]): The fields to add to each instance.
Use ‘/’ to access inner fields

use_deepcopy (bool) : Deep copy the input value to avoid later modifications

Examples:
# Add a ‘classes’ field with a value of a list “positive” and “negative” to all streams AddFields(fields={“classes”: [“positive”,”negatives”]})

# Add a ‘start’ field under the ‘span’ field with a value of 0 to all streams AddFields(fields={“span/start”: 0}

# Add a ‘classes’ field with a value of a list “positive” and “negative” to ‘train’ stream AddFields(fields={“classes”: [“positive”,”negatives”], apply_to_stream=[“train”]})

# Add a ‘classes’ field on a given list, prevent modification of original list # from changing the instance. AddFields(fields={“classes”: alist}), use_deepcopy=True) # if now alist is modified, still the instances remain intact.

Explanation about RenameFields¶

Renames fields.

Move value from one field to another, potentially, if field name contains a /, from one branch into another. Remove the from field, potentially part of it in case of / in from_field.

Examples:
RenameFields(field_to_field={“b”: “c”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: 2}, {“a”: 2, “c”: 3}]

RenameFields(field_to_field={“b”: “c/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: {“d”: 2}}, {“a”: 2, “c”: {“d”: 3}}]

RenameFields(field_to_field={“b”: “b/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “b”: {“d”: 2}}, {“a”: 2, “b”: {“d”: 3}}]

RenameFields(field_to_field={“b/c/e”: “b/d”}) will change inputs [{“a”: 1, “b”: {“c”: {“e”: 2, “f”: 20}}}] to [{“a”: 1, “b”: {“c”: {“f”: 20}, “d”: 2}}]

Explanation about InputOutputTemplate¶

Generate field ‘source’ from fields designated as input, and fields ‘target’ and ‘references’ from fields designated as output, of the processed instance.

Args specify the formatting strings with which to glue together the input and output designated fields of the processed instance into one string (‘source’ and ‘target’), and into a list of strings (‘references’).

References: processors.lower_case_till_punc, processors.take_first_non_empty_line, tasks.classification.multi_class.with_classes_descriptions