Conversational Exploration of Literature Landscape with LitChat

TL;DR

LitChat is an interactive literature exploration tool that combines large language models with data-driven discovery methods to enable comprehensive, objective, and transparent navigation of large-scale scientific literature.

Contribution

This paper introduces LitChat, a novel conversational agent that integrates LLMs with data-mining techniques for systematic literature exploration.

Findings

Effective navigation of large-scale literature landscapes

Ability to generate evidence-based insights

Case study demonstrates rapid literature exploration

Abstract

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.