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pretty_name: The ICL consistency test
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- 100K<n<1M
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---
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pretty_name: The ICL consistency test
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size_categories:
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- 100K<n<1M
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---
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# The ICL consistency test
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This 🤗 dataset provides data for the [GenBench CBT task 'The ICL consistency test'](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](https://arxiv.org/abs/2312.04945),[2023](https://aclanthology.org/2023.conll-1.20/)) 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](https://github.com/GenBench/genbench_cbt/blob/main/src/genbench/tasks/icl_consistency_test/).
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### Dataset Description
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_Abstract_: The ICL consistency test measures the consistency of LLM predictions on the same data points across many different prompting setups. Different setups are defined by "factors".
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On the one hand, factors can be specific attributes of the used prompt (e.g. the number of examples the model is presented with ["n_shots"] or the type of instructions
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that were used to wrap a specific datapoint ["Instructions"]). On the other hand, the analysis can also be augmented by factors that are related to the way a model is
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evaluated (e.g. whether a model is calibrated) or the type of model that is evaluated (e.g. the number of parameters or instructions tuning). These external factors can
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be added to the analysis by using the task.add_factor() method. The output metric is Cohen's kappa for each factor across all different conditions. A kappa value close to
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1 indicates that the factors do not change the model prediction, while a factor close to 0 strongly changes model predictions. The ICL consistency test has two subtasks,
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one evaluating the ANLI-dataset ([Nie et al., 2019](https://aclanthology.org/N18-1101/)); the other the MNLI-dataset ([Wang et al., 2017](https://aclanthology.org/N18-1101/)).
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_Size_: Each subtask contains 57600 when using the full 600 data_IDs. The user can choose to reduce the number of evaluated data_IDs.
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- **Curated by:**
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- resampling and arrangement was done by [Weber et al., 2023](https://arxiv.org/abs/2312.04945),[2023](https://aclanthology.org/2023.conll-1.20/);
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- original data were curated by [Nie et al., 2019](https://aclanthology.org/N18-1101/) (ANLI) and [Wang et al., 2017](https://aclanthology.org/N18-1101/) (MNLI);
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- templates were curated by [Bach et al., 2022](https://aclanthology.org/2022.acl-demo.9/) (promptsource).
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- **Language:** English
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### Dataset Sources (basic links)
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- **Repository:** Data files on [github](https://github.com/LucWeber/icl_consistency_data).
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- **Paper:** [Weber et al., 2023](https://arxiv.org/abs/2312.04945),[2023](https://aclanthology.org/2023.conll-1.20/).
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- **Demo:** Find pre-implemented code to evaluate any 🤗 model on [github](https://github.com/GenBench/genbench_cbt/blob/main/src/genbench/tasks/icl_consistency_test/example_evaluation.py).
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## Uses
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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. The ICL consistency test does this by measuring a model's prediction consistency across many different semantically equivalent prompting setups.
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## Dataset Structure
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits,
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relationships between data points, etc. -->
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[_TBA_]
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## Dataset Creation
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The data is a sample from the [MNLI](https://aclanthology.org/N18-1101/) and [ANLI](https://aclanthology.org/2020.acl-main.441/) datasets as well as prompt templates from [promptsource](https://aclanthology.org/2022.acl-demo.9/).
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Please refer to the original publications's documentation for detailed information on dataset creation.
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## Bias, Risks, and Limitations
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This dataset contains data from the [MNLI](https://aclanthology.org/N18-1101/) and [ANLI](https://aclanthology.org/2020.acl-main.441/) datasets and adheres to the same biases, risks and limitations.
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### Recommendations
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We identify the following limitations of the consistency test:
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1. The number of factors is limited and does not cover all possible factors that might influence the predictions. We limited ourselves to factors we deem relevant, to ensure fast evaluation.
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2. Currently, the test is only implemented for the ANLI- and MNLI-datasets.
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3. Factors that are external to the dataset but should be considered in the analysis (e.g. _instruction tuning_ or _calibration_) have to be manually added by the user
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using the task.add_factor() method (please use the GenBench implementation of the dataset. You can find it on [github](https://github.com/GenBench/genbench_cbt/tree/main/src/genbench/tasks/icl_consistency_test)).
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## Citation
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This dataset was used in the following publications. If you use it, please consider citing the following references:
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**BibTeX:**
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```
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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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```
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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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## Dataset Card Authors
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[Lucas Weber](https://lucweber.github.io/)
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## Dataset Card Contact
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