anli.py

"""
Adversarial NLI: A New Benchmark for Natural Language Understanding
https://arxiv.org/pdf/1910.14599.pdf

Adversarial NLI (ANLI) is a dataset collected via an iterative, adversarial
human-and-model-in-the-loop procedure. It consists of three rounds that progressively
increase in difficulty and complexity, and each question-answer includes annotator-
provided explanations.

Homepage: "https://github.com/facebookresearch/anli"

@inproceedings{nie-etal-2020-adversarial,
    title = "Adversarial {NLI}: A New Benchmark for Natural Language Understanding",
    author = "Nie, Yixin  and
      Williams, Adina  and
      Dinan, Emily  and
      Bansal, Mohit  and
      Weston, Jason  and
      Kiela, Douwe",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    year = "2020",
    publisher = "Association for Computational Linguistics",
}
"""
import numpy as np
from lm_eval.base import rf
from ..metrics import mean
from . common import HFTask


class ANLIBase(HFTask):
    VERSION = 0
    DATASET_PATH = "anli"
    DATASET_NAME = None
    SPLIT = None

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return True

    def training_docs(self):
        if self.has_training_docs():
            if self._training_docs is None:
                self._training_docs = list(self.data["train_r" + str(self.SPLIT)])
            return self._training_docs

    def validation_docs(self):
        if self.has_validation_docs():
            return self.data["dev_r" + str(self.SPLIT)]

    def test_docs(self):
        if self.has_test_docs():
            return self.data["test_r" + str(self.SPLIT)]

    def doc_to_text(self, doc):
        # OA does this a bit weirdly: they prepend "anli 1:  anli 1:  " to the beginning
        # of the prompt (yes, repeating it!). also, " True, False, or Neither?" is directly 
        # appended onto the question, with no "Answer:" or even a newline. Do we *really* 
        # want to do it exactly as OA did?
        return doc['premise'] + '\nQuestion: ' + doc['hypothesis'] + ' True, False, or Neither?\nAnswer:'

    def doc_to_target(self, doc):
        # True = entailment
        # False = contradiction
        # Neither = neutral
        return " " + ["True", "Neither", "False"][doc['label']]

    def construct_requests(self, doc, ctx):
        """ Uses RequestFactory to construct Requests and returns an iterable of 
        Requests which will be sent to the LM.

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param ctx: str
            The context string, generated by fewshot_context. This includes the natural 
            language description, as well as the few shot examples, and the question
            part of the document for `doc`. 
        """
        ll_true, _ = rf.loglikelihood(ctx, " True") 
        ll_neither, _ = rf.loglikelihood(ctx, " Neither") 
        ll_false, _ = rf.loglikelihood(ctx, " False") 
        return ll_true, ll_neither, ll_false
    
    def process_results(self, doc, results):
        """Take a single document and the LM results and evaluates, returning a 
        dict where keys are the names of submetrics and values are the values of 
        the metric for that one document

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param results:
            The results of the requests created in construct_requests.
        """
        gold = doc["label"]
        pred = np.argmax(results)
        return {
            "acc": pred == gold
        }

    def aggregation(self):
        """
        :returns: {str: [float] -> float}
            A dictionary where keys are the names of submetrics and values are 
            functions that aggregate a list of metrics
        """
        return {
            "acc": mean
        }

    def higher_is_better(self):
        """
        :returns: {str: bool}
            A dictionary where keys are the names of submetrics and values are 
            whether a higher value of the submetric is better
        """
        return {
            "acc": True
        }

class ANLIRound1(ANLIBase):
    SPLIT = 1

class ANLIRound2(ANLIBase):
    SPLIT = 2

class ANLIRound3(ANLIBase):
    SPLIT = 3