metadata
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 .
The dataset is formatted as a jsonl file (jsonlines-guide). 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}
}