Decoding Open-Ended Information Seeking Goals from Eye Movements in
  Reading
Authors: name: Cfir Avraham Hadar, name: Omer Shubi, name: Yoav Meiri, name: Yevgeni Berzak

When reading, we often have specific information that interests us in a text.
For example, you might be reading this paper because you are curious about LLMs
for eye movements in reading, the experimental design, or perhaps you only care
about the question ``but does it work?''. More broadly, in daily life, people
approach texts with any number of text-specific goals that guide their reading
behavior. In this work, we ask, for the first time, whether open-ended reading
goals can be automatically decoded from eye movements in reading. To address
this question, we introduce goal classification and goal reconstruction tasks
and evaluation frameworks, and use large-scale eye tracking for reading data in
English with hundreds of text-specific information seeking tasks. We develop
and compare several discriminative and generative multimodal LLMs that combine
eye movements and text for goal classification and goal reconstruction. Our
experiments show considerable success on both tasks, suggesting that LLMs can
extract valuable information about the readers' text-specific goals from eye
movements.