Revisiting Human Information Foraging: Adaptations for LLM-based Chatbots
<|reference_start|>Revisiting Human Information Foraging: Adaptations for LLM-based Chatbots: Information Foraging Theory's (IFT) framing of human information seeking choices as decision-theoretic cost-value judgments has successfully explained how people seek information among linked patches of information (e.g., linked webpages). However, the theory has to be adopted and validated in non-patchy LLM-based chatbot environments, before its postulates can be reliably applied to the design of such chat-based information seeking environments. This paper is a thought experiment that applies the IFT cost-value proposition to LLM-based chatbots and presents a set of preliminary hypotheses to guide future theory-building efforts for how people seek information in such environments.<|reference_end|>
@article{ragavan2024revisiting,
    title={Revisiting Human Information Foraging: Adaptations for LLM-based
  Chatbots},
    author={Sruti Srinivasa Ragavan, Mohammad Amin Alipour},
    journal={arXiv preprint arXiv:2406.04452},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04452},
    primaryClass={cs.HC}
}