# Revisiting Human Information Foraging: Adaptations for LLM-based Chatbots

arXiv: https://arxiv.org/abs/2406.04452 · HTML: https://mlsanity.com/paper/2406.04452/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions](https://mlsanity.com/paper/2504.18691/) (2025)

## Abstract (arXiv)

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.