SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model
This addresses the challenge of extracting inconsistent multimodal information for researchers, though it is incremental as it builds on existing LLM-based tools.
The authors tackled the problem of extracting structured data from scientific literature by introducing SciDaSynth, an interactive system using large language models, which in a study with researchers produced high-quality structured data more efficiently than baseline methods.
The explosion of scientific literature has made the efficient and accurate extraction of structured data a critical component for advancing scientific knowledge and supporting evidence-based decision-making. However, existing tools often struggle to extract and structure multimodal, varied, and inconsistent information across documents into standardized formats. We introduce SciDaSynth, a novel interactive system powered by large language models (LLMs) that automatically generates structured data tables according to users' queries by integrating information from diverse sources, including text, tables, and figures. Furthermore, SciDaSynth supports efficient table data validation and refinement, featuring multi-faceted visual summaries and semantic grouping capabilities to resolve cross-document data inconsistencies. A within-subjects study with nutrition and NLP researchers demonstrates SciDaSynth's effectiveness in producing high-quality structured data more efficiently than baseline methods. We discuss design implications for human-AI collaborative systems supporting data extraction tasks. The system code is available at https://github.com/xingbow/SciDaEx