Ashitasaxena
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README.md
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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) .
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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:
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```
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}
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```
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## Cite the work
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If you make use of the dataset or the code please cite our paper.
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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) .
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We collect a first-of-its-kind eye-tracking data, IITB-HGC, in which we employ 5 annotators for annotating 500 instances of claim-context pairs, carefully derived from the FactCC dataset (Kryscinski et al., 2020). During the annotation process, we capture the fixation durations of annotators on both the claim and
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context texts, along with their corresponding labels.
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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:
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```
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}
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```
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For the code, check our [github repository](https://github.com/kishanmaharaj/gaze-hallucination-detection/).
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## Cite the work
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If you make use of the dataset or the code please cite our paper.
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