Authors: Maciej P. Polak, Dane Morgan

Published on: March 07, 2023

Impact Score: 7.6

Arxiv code: Arxiv:2303.05352

Summary

• What is new: The introduction of ChatExtract method for automated data extraction from research papers, which minimizes the effort and expertise previously required.
• Why this is important: Manual data extraction from research papers is time-consuming and requires a lot of expertise and coding.
• What the research proposes: ChatExtract automates extracting data through engineered prompts with conversational LLMs, incorporating follow-up questions to ensure data accuracy.
• Results: Achieved precision and recall rates close to 90% using conversational LLMs, demonstrating high quality data extraction.

Technical Details

Technological frameworks used: Conversational LLMs

Models used: ChatGPT-4

Data used: Materials data, specifically for critical cooling rates of metallic glasses and yield strengths of high entropy alloys.

Potential Impact

Research institutions, academic publishers, and companies in the field of data extraction technologies could all see significant impacts from the adoption of ChatExtract.

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