Qlarify: Recursively Expandable Abstracts for Directed Information Retrieval over Scientific Papers

TL;DR

Qlarify introduces recursively expandable abstracts that dynamically incorporate additional information from full texts, enabling efficient, interactive exploration of scientific papers with AI-suggested expansions and source attribution.

Contribution

It presents a novel interaction paradigm for scientific literature that allows dynamic, AI-assisted expansion of abstracts to improve information retrieval and understanding.

Findings

User studies show improved exploration efficiency.

AI-generated expansions are accurate and verifiable.

Participants preferred interactive abstract exploration.

Abstract

Navigating the vast scientific literature often starts with browsing a paper's abstract. However, when a reader seeks additional information, not present in the abstract, they face a costly cognitive chasm during their dive into the full text. To bridge this gap, we introduce recursively expandable abstracts, a novel interaction paradigm that dynamically expands abstracts by progressively incorporating additional information from the papers' full text. This lightweight interaction allows scholars to specify their information needs by quickly brushing over the abstract or selecting AI-suggested expandable entities. Relevant information is synthesized using a retrieval-augmented generation approach, presented as a fluid, threaded expansion of the abstract, and made efficiently verifiable via attribution to relevant source-passages in the paper. Through a series of user studies, we demonstrate the utility of recursively expandable abstracts and identify future opportunities to support low-effort and just-in-time exploration of long-form information contexts through LLM-powered interactions.