Papers with human attention

2 papers
Analyzing Interpretability of Summarization Model with Eye-gaze Information (2024.lrec-main)

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Challenge: Existing studies have provided saliency scores for neural summarization models . eye-gaze information is often used as a proxy for human attention in reading tasks .
Approach: They propose to compare model saliency to human eye-gaze data to determine whether it conforms to human gaze during summarization.
Outcome: The proposed framework compares the model behavior to human summarization performance.
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze? (2022.acl-long)

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Challenge: We compare attention functions in pre-trained language models to human eye fixation patterns during task-specific reading tasks.
Approach: They compare attention functions in large-scale pre-trained language models to classical cognitive models of human attention by using a dataset with eye-tracking recordings of native speakers of English.
Outcome: The proposed model is as predictive of human eye fixation patterns as classical cognitive models of human attention.

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