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""" |
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This 🤗 dataset provides data for the GenBench CBT task 'The ICL consistency test' (see https://github.com/GenBench/genbench_cbt/tree/main/src/genbench/tasks/icl_consistency_test). |
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The ICL consistency test measures the consistency of LLM predictions on the same data points across many different equivalent prompting setups. |
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The score in the associated metric (Cohen's kappa) can be understood as a measure of a model's prediction consistency in the face of task-irrelevant information. |
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For an easy evaluation of any 🤗 models, we refer to the code provided in the GenBench task. For in-depth information on the task, we refer to the associated |
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publications (Weber et al., 2023,2023) and the respective GenBench doc.md (https://github.com/GenBench/genbench_cbt/blob/main/src/genbench/tasks/icl_consistency_test/doc.md). |
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Evaluation on the relevant metrics can be done via the example_evaluation.py script in the GenBench repository. |
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- Weber, L., Bruni, E., & Hupkes, D. (2023, December). Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning. |
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In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL) (pp. 294-313). |
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- Weber, L., Bruni, E., & Hupkes, D. (2023). The ICL Consistency Test. arXiv preprint arXiv:2312.04945. |
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""" |
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import json |
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import os |
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import datasets |
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_CITATION = """\ |
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@inproceedings{weber2023mind, |
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title={Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning}, |
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author={Weber, Lucas and Bruni, Elia and Hupkes, Dieuwke}, |
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booktitle={Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)}, |
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pages={294--313}, |
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year={2023} |
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}, |
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@article{weber2023icl, |
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title={The ICL Consistency Test}, |
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author={Weber, Lucas and Bruni, Elia and Hupkes, Dieuwke}, |
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journal={arXiv preprint arXiv:2312.04945}, |
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year={2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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In prompting, models are sensitive to task-irrelevant information in their prompt. This test can be used to quantify this sensitivity of any 🤗 model. |
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The ICL consistency test does this by measuring a model's prediction consistency across many different semantically equivalent prompting setups. |
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""" |
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_HOMEPAGE = "https://github.com/GenBench/genbench_cbt/blob/main/src/genbench/tasks/icl_consistency_test/doc.md" |
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_LICENSE = "" |
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_URL = "https://raw.githubusercontent.com/LucWeber/icl_consistency_data/main/data/" |
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_URLS = { |
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"anli": _URL + "genbench_all_anli.jsonl", |
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"mnli": _URL + "genbench_all_glue%2Bmnli.jsonl", |
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} |
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class ICLConsistencyTest(datasets.GeneratorBasedBuilder): |
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""" |
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In prompting, models are sensitive to task-irrelevant information in their prompt. This test can be used to quantify this sensitivity of any 🤗 model. |
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The ICL consistency test does this by measuring a model's prediction consistency across many different semantically equivalent prompting setups. |
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""" |
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VERSION = datasets.Version("1.1.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="anli", version=VERSION, description="This part of the ICL consistency test covers data points from ANLI"), |
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datasets.BuilderConfig(name="mnli", version=VERSION, description="This part of the ICL consistency test covers data points from MNLI"), |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"input": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"target_numeric": datasets.Value("int32"), |
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"data_ID": datasets.Value("int32"), |
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"setup_ID": datasets.Value("string") |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=("input", "target"), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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urls = _URLS[self.config.name] |
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data_dir = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": data_dir, |
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"split": "test"}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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with open(filepath, encoding="utf-8") as f: |
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for key, row in enumerate(f): |
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data = json.loads(row) |
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yield key, { |
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"input": data["input"], |
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"target": data["target"], |
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"target_numeric": data["target_numeric"], |
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"data_ID": data["data_ID"], |
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"setup_ID": data["setup_ID"], |
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} |
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