Conversational Exploration of Literature Landscape with LitChat
Mingyu Huang, Shasha Zhou, Yuxuan Chen, Ke Li

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…
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Taxonomy
TopicsTopic Modeling · Digital Mental Health Interventions · AI in Service Interactions
