Revisiting Human Information Foraging: Adaptations for LLM-based Chatbots

TL;DR

This paper explores how Information Foraging Theory can be adapted to understand human information seeking behavior in LLM-based chatbots, proposing hypotheses for future research.

Contribution

It applies IFT to chatbot environments, offering preliminary hypotheses to guide future empirical validation and theory development.

Findings

Proposes adaptation of IFT to chatbot contexts

Suggests new hypotheses for human-chatbot information foraging

Highlights need for empirical validation in non-patchy environments

Abstract

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.