AwesomeLit: Towards Hypothesis Generation with Agent-Supported Literature Research

TL;DR

AwesomeLit is a visualization system that supports hypothesis generation by providing transparent, user-guided exploration of literature, helping inexperienced researchers identify gaps and generate research ideas effectively.

Contribution

The paper introduces AwesomeLit, a novel human-agent collaborative system with visualizations and transparent workflows tailored for hypothesis generation in literature research.

Findings

Effective in helping users explore unfamiliar topics

Assists in identifying promising research directions

Improves confidence in research results

Abstract

There are different goals for literature research, from understanding an unfamiliar topic to generate hypothesis for the next research project. The nature of literature research also varies according to user's familiarity level of the topic. For inexperienced researchers, identifying gaps in the existing literature and generating feasible hypothesis are crucial but challenging. While general ``deep research'' tools can be used, they are not designed for such use case, thus often not effective. In addition, the ``black box" nature and hallucination of Large Language Models (LLMs) often lead to distrust. In this paper, we introduce a human-agent collaborative visualization system AwesomeLit to address this need. It has several novel features: a transparent user-steerable agentic workflow; a dynamically generated query exploring tree, visualizing the exploration path and provenance; and a semantic similarity view, depicting the relationships between papers. It enables users to transition from general intentions to detailed research topics. Finally, a qualitative study involving several early researchers showed that AwesomeLit is effective in helping users explore unfamiliar topics, identify promising research directions, and improve confidence in research results.