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README.md

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 pretty_name: The ICL consistency test
 size_categories:
 - 100K<n<1M
----
+---
+# The ICL consistency test
+
+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).
+The ICL consistency test measures the consistency of LLM predictions on the same data points across many different equivalent prompting setups. 
+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.
+
+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 
+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).
+
+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/).
+
+### Dataset Description
+
+_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". 
+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 
+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 
+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 
+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 
+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, 
+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/)).
+
+_Size_: Each subtask contains 57600 when using the full 600 data_IDs. The user can choose to reduce the number of evaluated data_IDs.
+
+- **Curated by:**
+    - resampling and arrangement was done by [Weber et al., 2023](https://arxiv.org/abs/2312.04945),[2023](https://aclanthology.org/2023.conll-1.20/);
+    - 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);
+    - templates were curated by [Bach et al., 2022](https://aclanthology.org/2022.acl-demo.9/) (promptsource). 
+- **Language:** English
+
+### Dataset Sources (basic links)
+
+- **Repository:** Data files on [github](https://github.com/LucWeber/icl_consistency_data).
+- **Paper:** [Weber et al., 2023](https://arxiv.org/abs/2312.04945),[2023](https://aclanthology.org/2023.conll-1.20/).
+- **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).
+
+## Uses
+
+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.
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, 
+relationships between data points, etc. -->
+
+[_TBA_]
+
+## Dataset Creation
+
+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/). 
+Please refer to the original publications's documentation for detailed information on dataset creation.
+
+## Bias, Risks, and Limitations
+
+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.
+
+### Recommendations
+
+We identify the following limitations of the consistency test:
+
+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.
+
+2. Currently, the test is only implemented for the ANLI- and MNLI-datasets.
+
+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
+   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)).
+
+
+## Citation
+
+This dataset was used in the following publications. If you use it, please consider citing the following references:
+
+**BibTeX:**
+
+```
+@inproceedings{weber2023mind,
+  title={Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning},
+  author={Weber, Lucas and Bruni, Elia and Hupkes, Dieuwke},
+  booktitle={Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)},
+  pages={294--313},
+  year={2023}
+}
+```
+```
+@article{weber2023icl,
+  title={The ICL Consistency Test},
+  author={Weber, Lucas and Bruni, Elia and Hupkes, Dieuwke},
+  journal={arXiv preprint arXiv:2312.04945},
+  year={2023}
+}
+```
+
+## Dataset Card Authors
+[Lucas Weber](https://lucweber.github.io/)
+
+## Dataset Card Contact
+[email protected]