|
--- |
|
license: apache-2.0 |
|
--- |
|
|
|
(Works with [Mobile-Env v3.x](https://github.com/X-LANCE/Mobile-Env/tree/v3.0).) |
|
|
|
# WikiHow Task Set |
|
|
|
WikiHow task set is an InfoUI interaction task set based on |
|
[Mobile-Env](https://github.com/X-LANCE/Mobile-Env) proposed in [*Mobile-Env: |
|
An Evaluation Platform and Benchmark for Interactive Agents in LLM |
|
Era*](https://arxiv.org/abs/2305.08144). |
|
[WikiHow](https://www.wikihow.com/Main-Page) is a collaborative wiki site about |
|
various real-life tips with more than 340,000 online articles. To construct the |
|
task set, 107,448 pages are crawled, and the dumped website data occupy about |
|
88 GiB totally. |
|
|
|
Several task definition templates are designed according to the functions of |
|
WikiHow app and task definitions are instantiated through the template toolkit |
|
in Mobile-Env. 577 tasks are sampled from the extended set, which is named the |
|
*canonical set* (`wikihow-canonical.tar.xz`). Owing to the limit of the |
|
budgets, only 150 tasks are tested using the proposed LLM-based agent. These |
|
150 tasks are given in `wikihow-microcanon.tar.xz`. We call it the *canonical |
|
subset* or the *micro canonical set*. |
|
|
|
### Website Data Replay |
|
|
|
The replay script for [mitmproxy](https://mitmproxy.org/) is given as |
|
`replay_url.py`. To use this replay script, the information retrieval tool |
|
[Pyserini](https://github.com/castorini/pyserini/) is required. Four parameters |
|
are expected to be assigned in the script: |
|
|
|
+ The crawled data from WikiHow website (`dumps` in `wikihow.data.tar.xz`) |
|
+ The HTML templates used to mock the search result page (`templates` in |
|
`wikihow.data.tar.xz`) |
|
+ The indices for the search engine based on Pyserini (`indices-t/indices` in |
|
`wikihow.data.tar.xz`) |
|
+ The metadata of the crawled articles (`indices-t/docs/doc_meta.csv` in |
|
`wikihow.data.tar.xz`) |
|
|
|
All the required data are offered in `wikihow.data.tar.xz`. (The archive is |
|
about 78 GiB. And the decompressed data are about 88 GiB.) The archive is split |
|
into two pieces (`wikihow.data.tar.xz.00` and `wikihow.data.tar.xz.01`). You |
|
can use `cat` to concatenate them: |
|
|
|
```sh |
|
cat wikihow.data.tar.xz.00 wikihow.data.tar.xz.01 >wikihow.data.tar.xz |
|
``` |
|
|
|
The SHA256 checksums are provided in `wikihow.data.tar.xz.sha256` to check the |
|
integrity. |
|
|
|
To run the script: |
|
|
|
```sh |
|
mitmproxy --showhost -s replay_url.py |
|
``` |
|
|
|
### Certificate Unpinning Plan |
|
|
|
The `syscert` plan proposed by Mobile-Env works for WikiHow app. You can |
|
complete the config according to the [guideline of |
|
Mobile-Env](https://github.com/X-LANCE/Mobile-Env/blob/master/docs/dynamic-app-en.md). |
|
The available APK package from [APKCombo](https://apkcombo.com/) is provided. |
|
And note to use the AVD image of version Android 11.0 (API Level 30) (Google |
|
APIs) to obtain the best compatibility and the root-enabled ADBD. |
|
|
|
### Human-Rewritten Instructions |
|
|
|
Human-rewritten instructions for the *canonical set* are release under |
|
`instruction_rewriting/`. An AndroidEnv wrapper `InstructionRewritingWrapper` |
|
is provided to load the rewritten instructions (`merged_doccano.json`) and |
|
public patterns (`pattern-*.txt`). The annotations are collected via |
|
[doccano](https://doccano.github.io/doccano/). The patterns are parsed by |
|
[`sentence_pattern.py`](instruction_rewriting/sentence_pattern.py). |
|
|
|
### Details of Sub-Tasks |
|
|
|
WikiHow taks are crafted from 16 types of sub-tasks: |
|
|
|
* `home2search`, instructing to search for an article from the home page. |
|
* `search2article`, `author2article`, & `category2article`, instructing to |
|
access an article from search result page, author information page, and |
|
category content page, respectively. |
|
* `article2about`, instructing to access the about page from article page. |
|
* `article2author`, instructing to access author information page from article |
|
page. |
|
* `article2category`, instructing to access category content page from article |
|
page. |
|
* `article2reference`, instructing to check reference list on article page. |
|
* `article2rate_no`, instructing to rate no for article |
|
* `article2rate_yes`, instructing to rate yes for article |
|
* `article2share`, instructing to share article |
|
* `article2bookmark`, instructing to bookmark article and then check the |
|
bookmarks. |
|
* `article2steps`, crafted from `stepped_summary` questions in |
|
[wikihow-lists](https://huggingface.co/datasets/b-mc2/wikihow_lists) |
|
* `article2ingredientes`, crafted from `ingredients` questions in |
|
[wikihow-lists](https://huggingface.co/datasets/b-mc2/wikihow_lists) |
|
* `article2needed_items`, crafted from `needed_items` questions in |
|
[wikihow-lists](https://huggingface.co/datasets/b-mc2/wikihow_lists) |
|
* `article2summary`, crafted from |
|
[WikiHowNFQA](https://huggingface.co/datasets/Lurunchik/WikiHowNFQA) tasks |
|
|
|
A template is composed for each sub-task, containing a group of filling slots |
|
expecting some keywords like article title, author name, question, and |
|
groundtruth answer. Then these keywords are sampled from the crawled app data |
|
or from the two QA datasets to instantiate the templates. Subsequently, the |
|
instantiated templates are concatenated into multi-stage task definitions under |
|
the constraint that the target page/element/answer (the part after `2`, *e.g.*, |
|
`share` from `article2share`) is directly on/referenced by the current page |
|
(the part before `2`, *e.g.*, `article` from `article2share`). Finally, we |
|
obtained the task set of 150 multistage tasks in which there are 2.68 |
|
single-stage sub-tasks averagely. |
|
|
|
The multistage tasks containing different sub-tasks are suffixed with different |
|
numbers. The meanings of suffixes and the number of suffixed tasks in the micro |
|
canonical set are list in the following table: |
|
|
|
| Suffix | Sub-tasks | #Tasks | |
|
|--------|------------------------------------------|--------| |
|
| 0 | `home-search-article-about` | 18 | |
|
| 1 | `home-search-article-rate_no` | 6 | |
|
| 2 | `home-search-article-rate_yes` | 10 | |
|
| 3 | `home-search-article-share` | 11 | |
|
| 4 | `home-search-article-author[-article]` | 7 | |
|
| 5 | `home-search-article-bookmark` | 13 | |
|
| 6 | `home-search-article-category[-article]` | 9 | |
|
| 7 | `home-search-article-reference` | 11 | |
|
| 8 | `home-search-article` | 25 | |
|
| 9 | `home-search-steps` | 15 | |
|
| 10 | `home-search-needed_items` | 10 | |
|
| 11 | `home-search-ingredients` | 5 | |
|
| 12 | `home-search-summary` | 10 | |
|
|
|
### About |
|
|
|
This task set is developed and maintained by [SJTU |
|
X-Lance](https://x-lance.sjtu.edu.cn/en). The corresponding paper is available |
|
at <https://arxiv.org/abs/2305.08144>. |
|
|
|
If you find WikiHow task set useful in your research, you can cite the project |
|
using the following BibTeX: |
|
|
|
```bibtex |
|
@article{DanyangZhang2023_MobileEnv_WikiHow, |
|
title = {{Mobile-Env}: An Evaluation Platform and Benchmark for LLM-GUI Interaction}, |
|
author = {Danyang Zhang and |
|
Lu Chen and |
|
Zihan Zhao and |
|
Ruisheng Cao and |
|
Kai Yu}, |
|
journal = {CoRR}, |
|
volume = {abs/2305.08144}, |
|
year = {2023}, |
|
url = {https://arxiv.org/abs/2305.08144}, |
|
eprinttype = {arXiv}, |
|
eprint = {2305.08144}, |
|
} |
|
``` |
|
|
