@@ -3,7 +3,7 @@ This list keeps track of which tasks' implementations have been ported to YAML /
Boxes should be checked iff tasks are implemented in the refactor and tested for regression. Tasks should be struck through if checked *against original introducing paper* implementation or popularizing implementation. (WIP) Denotes that there exists a PR or person working on this task already.
- [] Glue (Lintang)
- [x] Glue
- [x] SuperGlue
- [ ] CoQA (Lintang)
- [ ] DROP (Lintang)
...
...
@@ -13,12 +13,12 @@ Boxes should be checked iff tasks are implemented in the refactor and tested for
- [x] Wikitext
- [x] PiQA
- [x] PROST
- [] MCTACO (Lintang)
- [x] MCTACO
- [x] Pubmed QA
- [x] SciQ
- [ ] QASPER
- [x] QA4MRE
- [] TriviaQA (Lintang)
- [x] TriviaQA
- [x] AI2 ARC
- [x] LogiQA
- [x] HellaSwag
...
...
@@ -33,9 +33,9 @@ Boxes should be checked iff tasks are implemented in the refactor and tested for
- [x] Winogrande
- [x] ANLI
- [x] Hendrycks Ethics (missing some tasks/metrics, see PR 660: <https://github.com/EleutherAI/lm-evaluation-harness/pull/660> for more info)
- [x] TruthfulQA (mc1) (Lintang)
- [] TruthfulQA (mc2) (Lintang)
- [] TruthfulQA (gen) (Lintang)
- [x] TruthfulQA (mc1)
- [x] TruthfulQA (mc2)
- [x] TruthfulQA (gen)
- [ ] MuTual
- [ ] Hendrycks Math (Hailey)
- [ ] Asdiv
...
...
@@ -45,16 +45,16 @@ Boxes should be checked iff tasks are implemented in the refactor and tested for
*`ai2_arc`: Evaluates `arc_easy` and `arc_challenge`
#### Tasks
*`arc_easy`
*`arc_challange`
### Checklist
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] 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?
author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] 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?
Title: Towards ai-complete question answering: A set of prerequisite toy tasks
Abstract: https://arxiv.org/abs/1502.05698
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.
title={Towards ai-complete question answering: A set of prerequisite toy tasks},
author={Weston, Jason and Bordes, Antoine and Chopra, Sumit and Rush, Alexander M and Van Merri{\"e}nboer, Bart and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1502.05698},
year={2015}
}
```
### Groups and Tasks
#### Groups
* Not part of a group yet
#### Tasks
*`babi`
### Checklist
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] 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?
