---
license: mit
language:
- en
tags:
- gaze, hallucination
---

## Dataset Details

This repository consists the eye-tracking dataset released as part of EMNLP 2023 paper: [Eyes Show the Way: Modelling Gaze Behaviour for Hallucination Detection](https://aclanthology.org/2023.findings-emnlp.764.pdf) . 

The dataset is formatted as a jsonl file ([jsonlines-guide](https://jsonlines.org/)). Each line can be loaded as a json object, and has the following format:

```
{
    'trial_id'            :   <instance id (can take values from 1-500)>
    'text'                :   <the text presented to the annotator, in a claim:context format>
    'participant_id'      :   <participant id (p01, p02, p03, p04, p05)>
    'fixation_seqs'       :   <sequence ids of fixations>
    'fixation_word_ids'   :   <a sequence containing the ids of the words being fixated in the order in which they were fixated upon>
    'fixation_word_texts' :   <a sequence containing the words being fixated in the order in which they were fixated upon>
    'fixation_durations'  :   <a sequence containing the fixation durations (in ms) in the order in which the words were fixated upon>
    'annotator_labels'    :   <label given by the annotator (can take a value: hallucinated/non_hallucinated)>
    'true_labels'         :   <true label from the FactCC dataset (can take a value: hallucinated/non_hallucinated)>
}
```

## Cite the work

If you make use of the dataset or the code please cite our paper.

```
@inproceedings{maharaj2023eyes,
  title={Eyes Show the Way: Modelling Gaze Behaviour for Hallucination Detection},
  author={Maharaj, Kishan and Saxena, Ashita and Kumar, Raja and Mishra, Abhijit and Bhattacharyya, Pushpak},
  booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
  year={2023}
}
```