A Measure Based Generalizable Approach to Understandability

Successful agent-human partnerships require that any agent generated information is understandable to the human, and that the human can easily steer the agent towards a goal. Such effective communication requires the agent to develop a finer-level notion of what is understandable to the human. State-of-the-art agents, including LLMs, lack this detailed notion of understandability because they only capture average human sensibilities from the training data, and therefore afford limited steerability (e.g., requiring non-trivial prompt engineering).In this paper, instead of only relying on data, we argue for developing generalizable, domain-agnostic measures of understandability that can be used as directives for these agents. Existing research on understandability measures is fragmented, we survey various such efforts across domains, and lay a cognitive-science-rooted groundwork for more coherent and domain-agnostic research investigations in future.

@article{kushwaha2025_2503.21615,
  title={ A Measure Based Generalizable Approach to Understandability },
  author={ Vikas Kushwaha and Sruti Srinivasa Ragavan and Subhajit Roy },
  journal={arXiv preprint arXiv:2503.21615},
  year={ 2025 }
}