Human-like attention as a supervisory signal to guide neural attention has shown significant
promise but is currently limited to unimodal integration-even for inherently multimodal tasks …

Detecting hallucinations in natural language processing (NLP) is a critical undertaking that
demands a deep understanding of both the semantic and pragmatic aspects of languages …

Relative word importance is a key metric for natural language processing. In this work, we
compare human and model relative word importance to investigate if pretrained neural …

Eye movements in reading reveal humans' cognitive processes involved in language
understanding. The duration a reader's eyes fixate on a word has been used as a measure …

This research delves into the intricate connection between self-attention mechanisms in
large-scale pre-trained language models, like BERT, and human gaze patterns, with the aim …

Advancements in Natural Language Processing (NLP), have led to the emergence of Large
Language Models (LLMs) such as GPT, Llama, Claude, and Gemini, which excel across a …

Human fixation patterns have been shown to correlate strongly with Transformer-based
attention. Those correlation analyses are usually carried out without taking into account …

The Eye Movements on Machine-Generated Texts Corpus (EMTeC) is a naturalistic eye-
movements-while-reading corpus of 107 native English speakers reading machine …

Rationales in the form of manually annotated input spans usually serve as ground truth
when evaluating explainability methods in NLP. They are, however, time-consuming and …

We proactively direct our eyes and attention to collect information during problem solving
and decision making. Understanding gaze patterns is crucial for gaining insights into the …