# SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model

arXiv: https://arxiv.org/abs/2404.13765 · HTML: https://mlsanity.com/paper/2404.13765/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 66 references that are in the map.
- First posted on arXiv 21 Apr 2024.

## Builds on (most cited first, 22 of 22)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://mlsanity.com/paper/2105.03011/) (2021)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [Teaching language models to support answers with verified quotes](https://mlsanity.com/paper/2203.11147/) (2022)
- [RARR: Researching and Revising What Language Models Say, Using Language Models](https://mlsanity.com/paper/2210.08726/) (2022)
- [Measuring Attribution in Natural Language Generation Models](https://mlsanity.com/paper/2112.12870/) (2021)
- [Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models](https://mlsanity.com/paper/2212.08037/) (2022)
- [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://mlsanity.com/paper/1906.04701/) (2019)
- [Automatic Evaluation of Attribution by Large Language Models](https://mlsanity.com/paper/2305.06311/) (2023)
- [Inferring Which Medical Treatments Work from Reports of Clinical Trials](https://mlsanity.com/paper/1904.01606/) (2019)
- [Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols](https://mlsanity.com/paper/2009.14237/) (2020)
- [Scim: Intelligent Skimming Support for Scientific Papers](https://mlsanity.com/paper/2205.04561/) (2022)
- [Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking](https://mlsanity.com/paper/2308.07517/) (2023)
- [Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing](https://mlsanity.com/paper/2203.00130/) (2022)
- [Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature](https://mlsanity.com/paper/2208.03455/) (2022)
- [Conversations with Documents. An Exploration of Document-Centered Assistance](https://mlsanity.com/paper/2002.00747/) (2020)
- [Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time](https://mlsanity.com/paper/2005.10865/) (2020)
- [Beyond Interestingness: Semantic and Context-Aware Natural Language Query Recommendations for Visual Data Analysis](https://mlsanity.com/paper/2201.04868/) (2022)
- [Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool](https://mlsanity.com/paper/2207.08401/) (2022)

## Built on by (most cited first, 3 of 3)

- [Mathematical Derivation Graphs: A Relation Extraction Task in STEM Manuscripts](https://mlsanity.com/paper/2410.21324/) (2024)
- [Building Agent Harnesses for Scientific Curation from Multimodal Sources](https://mlsanity.com/paper/2606.21005/) (2026)
- [Darth Vecdor: An Open-Source System for Generating Knowledge Graphs Through Large Language Model Queries](https://mlsanity.com/paper/2512.15906/) (2025)

## Abstract (arXiv)

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