AwesomeLit: Towards Hypothesis Generation with Agent-Supported Literature Research
It addresses the problem of inefficient and untrustworthy literature research for inexperienced researchers, though it appears incremental as it builds on existing visualization and agent-based approaches.
The paper tackles the challenge of helping inexperienced researchers identify literature gaps and generate hypotheses by introducing AwesomeLit, a human-agent collaborative visualization system, which a qualitative study showed effectively aids users in exploring unfamiliar topics and identifying promising research directions.
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.