---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: answer
    dtype: string
  - name: type
    dtype: string
  - name: level
    dtype: string
  - name: supporting_facts
    sequence:
    - name: title
      dtype: string
    - name: sent_id
      dtype: int32
  - name: context
    sequence:
    - name: title
      dtype: string
    - name: sentences
      sequence: string
  - name: problem
    dtype: string
  - name: source
    dtype: string
  - name: gold_removed
    dtype: int64
  - name: removed_titles
    sequence: string
  splits:
  - name: train
    num_bytes: 226320070.31742346
    num_examples: 20000
  - name: test
    num_bytes: 5716501.080351114
    num_examples: 500
  download_size: 116725004
  dataset_size: 232036571.39777458
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---
This dataset is a modified version of the HotPotQA distractor dataset, which contains factual questions requiring multi-hop reasoning. 
In the original HotPotQA dataset, each example presents ten paragraphs, only two of which contain the information necessary to answer the question; the remaining eight paragraphs include closely related but irrelevant details. 
Consequently, solving this task requires the model to identify and reason over the pertinent passages.
To more strongly develop uncertainty reasoning capability, we construct  *HotPotQA-Modified*, in which we systematically remove either 0, 1, or both of the key paragraphs required to answer each question. 
This modification introduces varying levels of informational completeness that the model must reason over. 
The "gold_removed" column indicates the number of relevant paragraphs removed (0,1 or 2).
Questions are distributed across three equal groups: one-third have no relevant paragraphs (0/8), one-third have 1 relevant paragraph (1/7), and one-third have both relevant paragraphs (2/6). Each question consistently contains 8 total paragraphs.

To cite the original HotPotQA dataset: 
```bibtex
@article{yang2018hotpotqa,
  title={HotpotQA: A dataset for diverse, explainable multi-hop question answering},
  author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W and Salakhutdinov, Ruslan and Manning, Christopher D},
  journal={arXiv preprint arXiv:1809.09600},
  year={2018}
}
```

To cite our modified dataset:

```bibtex
@article{damani2025beyond,
  title={Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty},
  author={Damani, Mehul and Puri, Isha and Slocum, Stewart and Shenfeld, Idan and Choshen, Leshem and Kim, Yoon and Andreas, Jacob},
  journal={arXiv preprint arXiv:2507.16806},
  year={2025}
}
```