Title: `COMMONSENSEQA: A Question Answering Challenge Targeting
Commonsense Knowledge`
Abstract: https://arxiv.org/pdf/1811.00937.pdf
CommonsenseQA is a multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers.
It contains 12,102 questions with one correct answer and four distractor answers.
Homepage: https://www.tau-nlp.org/commonsenseqa
### Citation
```
@inproceedings{talmor-etal-2019-commonsenseqa,
title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
author = "Talmor, Alon and
Herzig, Jonathan and
Lourie, Nicholas and
Berant, Jonathan",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1421",
doi = "10.18653/v1/N19-1421",
pages = "4149--4158",
archivePrefix = "arXiv",
eprint = "1811.00937",
primaryClass = "cs",
}
```
### Groups and Tasks
#### Groups
* Not part of a group yet.
#### Tasks
*`commonsense_qa`: Represents the "random" split from the paper. Uses an MMLU-style prompt, as (presumably) used by Llama evaluations.
### Checklist
For adding novel benchmarks/datasets to the library:
* [x] Is the task an existing benchmark in the literature?
* [x] Have you referenced the original paper that introduced the task?
* [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
This release is the simplified version of FLD where a model is required to predict only an answer.
This setting is described by "answer accuracy" in the original paper.
#### Tasks in Group `fld`
*`fld_default` is a basic task based on [FLD.v2](https://huggingface.co/datasets/hitachi-nlp/FLD.v2/viewer/star)
*`fld_star`: is a more challenging version based on [FLD.v2-star](https://huggingface.co/datasets/hitachi-nlp/FLD.v2/viewer/star)
#### Tasks in Group `fld_logical_formula`
Further, we have "logical formula" versions of the benchmarks, which evaluate LLMs' pure logical reasoning capabilities within the domain of logical formulas, rather than natural language:
*`fld_logical_formula_default`
*`fld_logical_formula_fld_star`
### Checklist
For adding novel benchmarks/datasets to the library:
The goal of this benchmark is to isolate the test answering capabilities from the content knowledge.
### Paper
Title: Multiple Choice Questions and Large Languages Models: A Case Study with Fictional Medical Data
Abstract: https://arxiv.org/abs/2406.02394
To test the relevance of MCQs to assess LLM performance without prior data exposure, we created a fictional medical benchmark and knowledge base on a non-existent gland, the Glianorex. Using GPT-4 we generated a comprehensive textbook on the Glianorex in both English and French, and created multiple-choice questions in both English and French.
### Tasks
All tasks are multiple choice questions with 4 options, only one correct option.
-`glianorex`: Evaluates all tasks listed below.
-`glianorex_en`: Evaluates the accuracy on 264 questions in English.
-`glianorex_fr`: Evaluates the accuracy on 264 questions in French.
