Conversational Exploration of Literature Landscape with LitChat
This addresses the problem of literature navigation for researchers in fields like AI4Health, offering a novel tool for systematic reviews, though it is incremental as it builds on existing LLM and data-mining techniques.
The paper tackles the challenge of exploring large-scale scientific literature by introducing LitChat, an interactive conversational agent that combines LLMs with data-driven tools to provide evidence-based insights, demonstrated through a case study on AI4Health.
We are living in an era of "big literature", where the volume of digital scientific publications is growing exponentially. While offering new opportunities, this also poses challenges for understanding literature landscapes, as traditional manual reviewing is no longer feasible. Recent large language models (LLMs) have shown strong capabilities for literature comprehension, yet they are incapable of offering "comprehensive, objective, open and transparent" views desired by systematic reviews due to their limited context windows and trust issues like hallucinations. Here we present LitChat, an end-to-end, interactive and conversational literature agent that augments LLM agents with data-driven discovery tools to facilitate literature exploration. LitChat automatically interprets user queries, retrieves relevant sources, constructs knowledge graphs, and employs diverse data-mining techniques to generate evidence-based insights addressing user needs. We illustrate the effectiveness of LitChat via a case study on AI4Health, highlighting its capacity to quickly navigate the users through large-scale literature landscape with data-based evidence that is otherwise infeasible with traditional means.