Datasets:

Modalities:
Text
Formats:
text
Size:
< 1K
ArXiv:
Libraries:
Datasets
License:
File size: 7,325 Bytes
0adf04e
 
 
30150ff
89ab7b8
7c38fdd
30150ff
 
c38963e
 
 
 
 
 
 
 
30150ff
 
12d83cf
 
 
 
 
 
30150ff
 
 
 
 
c38963e
30150ff
 
 
c38963e
30150ff
 
 
c38963e
30150ff
 
3645b03
554dcea
 
 
 
 
 
 
 
c38963e
554dcea
30150ff
 
 
 
 
 
 
 
 
c38963e
 
30150ff
c38963e
 
30150ff
d58c721
 
 
 
 
 
 
 
 
946d938
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8adac26
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
946d938
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
---
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},
}
```