task_categories:question-answering task_categories:text-generation size_categories:10K<n<100K language:en region:us
---
task_categories:
- question-answering
- text-generation
language:
- en
pretty_name: Multi-hop Question Answering
size_categories:
- 10K<n<100K
---

# Dataset Card for QA-Expert-multi-hop-qa-V1.0
This dataset aims to provide training data for the task: Question Answering, with a focus on <b>Multi-hop Question Answering</b>. 
In total, our training dataset contains 27720 data points (train & validation) including *single questions* and *multi-hop questions*.
You can take a look at the model we trained on this data: [https://huggingface.co/khaimaitien/qa-expert-7B-V1.0](https://huggingface.co/khaimaitien/qa-expert-7B-V1.0)

Example of single question: what is the capital city of Vietnam?
Example of multi-hop question: what is the population of the capital city of Vietnam?

## Dataset Details

### Dataset Description

To have more information (prompts we used and code) about the way we create our dataset, please take a look at: [https://github.com/khaimt/qa_expert/tree/main/train#training-data](https://github.com/khaimt/qa_expert/tree/main/train#training-data)

This training dataset contains 22720 data points from several sources:
#### Single Questions
We use single questions from the following sources:
  + [Squad](https://huggingface.co/datasets/squad_v2): We randomly select 4000 answerable questions + 2400 unanswerable questions.
    As the answers to these questions are spans, which are short, so we use OpenAI model to generate a complete answer given the question and context.
  + [BoolQ](https://huggingface.co/datasets/boolq): We randomly select 1600 random questions.
    As the answers of these questions are yes/no, so we also use OpenAI model to generate complete answers.
    This type of question is more difficult and needs reasoning (like Chain-of-Thought), so we ask the model to first generate the reasoning and then the final answer.
  + [Drop](https://huggingface.co/datasets/drop): We randomly select 1600 random questions.
    The answers of these questions are also short and without explanation.
    So we also use OpenAI model to generate the reasoning, arithmetic sequence (if needed) and the final answer.
#### Multi-hop Questions
For multi-hop questions, we use [Musique](https://github.com/StonyBrookNLP/musique) and generated data.
  + [Musique]: This dataset is from: [Musique](https://github.com/StonyBrookNLP/musique).
    The authors built these multi-hop questions based on single questions from various sources such as squad2, natural questions, zerore, ...
    But we found that some single questions are not well-formed (not a question and containing: <b>">>"</b>), such as "Stadio Ciro Vigorito >> occupant". So we removed all data points that had at least one unwell-formed single question.
    Finally, we attained 5847 answerable multi-hop questions and we also randomly selected 2400 unanswerable multi-hop questions.
    Each multi-hop question is decomposed into 2 or more single questions, and each single question contains short span answer.
    so Here is how we process the data:
      + First, for each single question, we generate the complete answer using OpenAI model.
      + Next, we generate final answer based on the <b>generated answers</b> from single questions.
  + Our generated data: We used openAI model to generate multi-hop questions, please take a look at here: [https://github.com/khaimt/qa_expert/tree/main/train#generate-training-data](https://github.com/khaimt/qa_expert/tree/main/train#generate-training-data)


### Format 
Each data point is a Json:
+ *src*: source of data point: squad.json, drop.json, boolq.json, musicque.json or gen_qa.json
+ *question*: the question, either single question or multi-hop questions
+ *inal_answer*: the final answer of the question --> model will generate this answer in the end
+ *answer*: span answer or None --> please ignore this, just an additional field of information
+ *sub_questions*: List of single questions to answer to answer the multi-hop question. If len(sub_questions) == 1 --> this is single question, not multi-hop question
    + *question*: the single question to ask
    + *answer*: the span answer of None or missing --> please ignore this, just an additional field of information
    + *long_answer*: the complete answer of this single question
    + *paragraph*: the context of the single question (this is considered as the retrieved context of the single question)
    + *unanswerable*: = True if this question is unanswerable --> you can ignore this because long_answer, note this field might be missing, default value is False.


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## Citation [optional]

```
@Misc{qa-expert,
      title={QA Expert: LLM for Multi-hop Question Answering},
      author={Khai Mai},
      howpublished={\url{https://github.com/khaimt/qa_expert}},
      year={2023},
}
```
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