Update README.md

README.md

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----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-sa-4.0
+task_categories:
+- question-answering
+language:
+- en
+pretty_name: FanoutQA
+size_categories:
+- 1K<n<10K
+---
+
+# FanOutQA
+
+![PyPI](https://img.shields.io/pypi/v/fanoutqa)
+
+[Read the paper!](https://aclanthology.org/2024.acl-short.2/) | [Download the dataset!](https://github.com/zhudotexe/fanoutqa/tree/main/fanoutqa/data)
+
+> [!NOTE]
+> This HuggingFace repo is a mirror of our GitHub repo's data. We recommend using the `fanoutqa` Python package to interact with the dataset.
+
+FanOutQA is a high quality, multi-hop, multi-document benchmark for large language models using English Wikipedia as its
+knowledge base. Compared to other question-answering benchmarks, FanOutQA requires reasoning over a greater number of
+documents, with the benchmark's main focus being on the titular fan-out style of question. We present these questions
+in three tasks -- closed-book, open-book, and evidence-provided -- which measure different abilities of LLM systems.
+
+This repository contains utilities to download and work with the dataset in Python, along with implementations of the
+evaluation metrics presented in our paper. Alternatively, you can download the dev and test sets in JSON format and
+generate completions to submit to us for evaluation.
+
+To view the leaderboards and more documentation about how to use this dataset, check out our website
+at <https://fanoutqa.com>!
+
+## Requirements and Installation
+
+The `fanoutqa` package requires Python 3.8+.
+
+To work with just the data, use `pip install fanoutqa`.
+Use `pip install "fanoutqa[all]"` and read the following section to include a baseline retriever and evaluation metrics.
+
+### Optional
+
+To include a baseline BM25-based retriever, use `pip install "fanoutqa[retrieval]"`.
+
+To run evaluations on the dev set, you will need to run a couple more steps:
+
+```shell
+pip install "fanoutqa[eval]"
+python -m spacy download en_core_web_sm
+pip install "bleurt @ git+https://github.com/google-research/bleurt.git@master"
+wget https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip
+unzip BLEURT-20.zip
+rm BLEURT-20.zip
+```
+
+## Quickstart
+
+1. Use `fanoutqa.load_dev()` or `fanoutqa.load_test()` to load the dataset.
+2. Run your generations.
+    1. Use `fanoutqa.wiki_search(title)` and `fanoutqa.wiki_content(evidence)` to retrieve the contents of
+       Wikipedia pages for the Open Book and Evidence Provided settings.
+3. Evaluate your generations with `fanoutqa.eval.evaluate(dev_questions, answers)` (see below for the schema).
+
+## Data Format
+
+To load the dev or test questions, simply use `fanoutqa.load_dev()` or `fanoutqa.load_test()`. This will return a list
+of `DevQuestion` or `TestQuestion`, as documented below.
+
+### Common Models
+
+```python
+Primitive = bool | int | float | str
+
+
+class Evidence:
+    pageid: int  # Wikipedia page ID
+    revid: int  # Wikipedia revision ID of page as of dataset epoch
+    title: str  # Title of page
+    url: str  # Link to page
+```
+
+### Dev Set
+
+The development set is a JSON file containing a list of DevQuestion objects:
+
+```python
+class DevQuestion:
+    id: str
+    question: str  # the top-level question to answer
+    decomposition: list[DevSubquestion]  # human-written decomposition of the question
+    answer: dict[str, Primitive] | list[Primitive] | Primitive
+    necessary_evidence: list[Evidence]
+    categories: list[str]
+
+
+class DevSubquestion:
+    id: str
+    question: str
+    decomposition: list[DevSubquestion]
+    answer: dict[str, Primitive] | list[Primitive] | Primitive  # the answer to this subquestion
+    depends_on: list[str]  # the IDs of subquestions that this subquestion requires answering first
+    evidence: Evidence | None  # if this is None, the question will have a decomposition
+```
+
+### Test Set
+
+The test set contains a slightly different format, as the answers are not provided. We include links to all the evidence
+used in the human-written decompositions for our Evidence Provided task.
+
+```python
+class TestQuestion:
+    id: str
+    question: str
+    necessary_evidence: list[Evidence]
+    categories: list[str]
+```
+
+## Wikipedia Retrieval
+
+To retrieve the contents of Wikipedia pages used as evidence, this package queries Wikipedia's Revisions API. There
+are two main functions to interface with Wikipedia:
+
+- `wiki_search(query)` returns a list of Evidence (Wikipedia pages that best match the query)
+- `wiki_content(evidence)` takes an Evidence and returns its content (as of the dataset epoch) as Markdown.
+
+To save on time waiting for requests and computation power (both locally and on Wikipedia's end), this package
+aggressively caches retrieved Wikipedia pages. By default, this cache is located in `~/.cache/fanoutqa/wikicache`.
+We provide many cached pages (~9GB) you can prepopulate this cache with, by using the following commands:
+
+```shell
+mkdir -p ~/.cache/fanoutqa
+wget -O ~/.cache/fanoutqa/wikicache.tar.gz https://datasets.mechanus.zhu.codes/fanoutqa/wikicache.tar.gz 
+tar -xzf ~/.cache/fanoutqa/wikicache.tar.gz
+```
+
+## Evaluation
+
+To evaluate a model's generation, first ensure that you have installed all the evaluation dependencies (see above).
+
+To use the GPT-as-judge metric, you will need to provide your OpenAI API key. We intentionally do not read
+the `OPENAI_API_KEY` environment variable by default to prevent accidentally spending money; you must set the
+`FANOUTQA_OPENAI_API_KEY` environment variable instead. You can use `export FANOUTQA_OPENAI_API_KEY=$OPENAI_API_KEY` to
+quickly copy it over.
+
+You should record your model/system's outputs as a list of dicts with the following schema:
+
+```json
+{
+  "id": "The ID of the question (see test set schema) this is a generation for.",
+  "answer": "The model's generation."
+}
+```
+
+Finally, to evaluate your generations on the dev set,
+call `fanoutqa.eval.evaluate(dev_questions, answers, llm_cache_key="your-model-key")`. This will run all of the metrics
+and return an `EvaluationScore` object, which has attributes matching the following structure:
+
+```json
+{
+  "acc": {
+    "loose": 0,
+    "strict": 0
+  },
+  "rouge": {
+    "rouge1": {
+      "precision": 0,
+      "recall": 0,
+      "fscore": 0
+    },
+    "rouge2": {
+      "precision": 0,
+      "recall": 0,
+      "fscore": 0
+    },
+    "rougeL": {
+      "precision": 0,
+      "recall": 0,
+      "fscore": 0
+    }
+  },
+  "bleurt": 0,
+  "gpt": 0
+}
+```
+
+(to access this in this dictionary form, use `dataclasses.asdict()`.)
+
+### Test Set Evaluation
+
+To evaluate your model on the hidden test set, first generate answers for each question in the test set.
+Your generations should be in the form of a JSONL file, with each line being a JSON object with the following schema for
+each test question:
+
+```json
+{
+  "id": "The ID of the question (see test set schema) this is a generation for.",
+  "answer": "The model's generation."
+}
+```
+
+You will also need to write a metadata file for your model. Your metadata file should use this template:
+
+```json
+{
+  "name": "The name of your model",
+  "authors": "The list of authors, in natural language (e.g. `Andrew Zhu, Alyssa Hwang, Liam Dugan, and Chris Callison-Burch`)",
+  "url": "A link to your model's website, if applicable (null otherwise)",
+  "citation": "The list of authors and year, in citation format (e.g. `Zhu et al., 2024`)",
+  "type": "FOUNDATION | FINETUNE | PROMPT | OTHER",
+  "context": "The context length of the model your system uses (as an int)",
+  "is_trained_for_function_calling": "Whether your model was trained for function calling specifically (true/false)",
+  "details": "Additional model details (e.g. API model revision or Hugging Face model ID) - optional",
+  "closedbook_generations": "YOUR-SYSTEM-NAME.jsonl",
+  "openbook_generations": "YOUR-SYSTEM-NAME.jsonl",
+  "evidenceprovided_generations": "YOUR-SYSTEM-NAME.jsonl"
+}
+```
+
+Then, fork this repository. Add your generation files to
+`leaderboard-submissions/[SETTING]-generations/YOUR-SYSTEM-NAME.jsonl`
+and your metadata file to `leaderboard-submissions/metadata/YOUR-SYSTEM-NAME.json` and make a pull request.
+
+If you do not want to release the generations of your model, please email these files to
+[[email protected]](mailto:[email protected]) instead and we will add your model to the leaderboards without
+pushing the generations.
+
+Finally, our GitHub bot will automatically run metrics on the submitted generations and commit a metrics file to
+`leaderboard-submissions/results/YOUR-SYSTEM-NAME.jsonl`. If all looks well, a maintainer will merge the PR and your
+model will appear on the leaderboards!
+
+## Additional Resources
+
+Although this package queries live Wikipedia and uses the Revisions API to get page content as of the dataset epoch,
+we also provide a snapshot of English Wikipedia as of Nov 20, 2023. You can download this
+snapshot [here](https://datasets.mechanus.zhu.codes/fanoutqa/enwiki-20231120-pages-articles-multistream.xml.bz2) (23G)
+and its
+index [here](https://datasets.mechanus.zhu.codes/fanoutqa/enwiki-20231120-pages-articles-multistream-index.txt.bz2).
+
+## Acknowledgements
+
+We would like to thank the members of the lab of Chris Callison-Burch for detailed feedback on the contents of this
+paper and the members of the Northern Lights Province Discord for their participation in our human evaluation. In
+particular, we would like to thank Bryan Li for his thoughtful suggestions with regards to our human evaluation and
+other parts of our paper.
+
+This research is supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced
+Research Projects Activity (IARPA), via the HIATUS Program contract #2022-22072200005. The views and conclusions
+contained herein are those of the authors and should not be interpreted as necessarily representing the official
+policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to
+reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.