title: Decoding Open-Ended Information Seeking Goals from Eye Movements in
    Reading
summary: 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.
paper_authors._id: 681b03a33bf166767107c74b
paper_authors.name: Cfir Avraham Hadar
paper_authors._id: 681b03a33bf166767107c74c
paper_authors.name: Omer Shubi
paper_authors._id: 681b03a33bf166767107c74d
paper_authors.name: Yoav Meiri
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-08T08:59:32.849Z
paper_authors._id: 6304be28bad6ce7fc026f55f
paper_authors.avatarUrl: /avatars/0597579c2f0e48f42b636997ec174004.svg
paper_authors.fullname: Yoav Meiri
paper_authors.type: user
paper_authors.user: YoavMeiri
paper_authors._id: 681b03a33bf166767107c74e
paper_authors.name: Yevgeni Berzak
paper_title: Decoding Open-Ended Information Seeking Goals from Eye Movements in
    Reading
paper_summary: 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.
paper_ai_summary: LLMs can ascertain readers' specific information goals through eye movement data, as demonstrated by successful goal classification and reconstruction tasks.
paper_ai_keywords: LLMs eye movements goal classification goal reconstruction multimodal large-scale eye tracking