A long-standing goal in the data management community is to develop automated systems that can process semi-structured documents and output queryable tables without the need for human effort or domain-specific customization. However, due to the wide variety of potential documents, current systems often make simplifying assumptions and rely on domain-specific training. In this study, the researchers explore whether large language models (LLMs) can help achieve generality in this task. The researchers propose a prototype system called EVAPORATE, which is powered by LLMs. They investigate two different strategies for implementing this system: directly extracting values from documents using LLMs or synthesizing code that performs the extraction. Through evaluations, they find that there is a tradeoff between cost and quality with these approaches. While code synthesis is cheaper, it is less accurate compared to directly processing each document with LLMs. To improve the quality while maintaining low cost, the researchers introduce an extended code synthesis implementation called EVAPORATE-CODE+. This approach achieves better quality than direct extraction by generating multiple candidate functions and ensembling their extractions using weak supervision. EVAPORATE-CODE+ not only outperforms state-of-the-art systems but also reduces the number of tokens the LLM needs to process by 110 times on average across various evaluation settings. The study focuses on developing systems that can convert heterogeneous data sources into structured tables for analytical queries. These data sources include webpages, PDFs, and text documents. The proposed systems aim to identify the schema of these documents and perform extraction to populate tables automatically. As an example application, medical researchers often analyze data from electronic health records (EHR), clinical trials, PubMed knowledge sources, and FDA reports. The researchers consider FDA 510(k) reviews for premarket notification submissions as a motivating setting. Their objective is to automatically structure attributes related to medical devices in these reviews and output a table format. Overall, the study demonstrates that LLM-based systems can effectively generate structured views of heterogeneous data. The proposed EVAPORATE-CODE+ system achieves high-quality results while minimizing the processing load on the LLM.
- Long-standing goal: Develop automated systems for processing semi-structured documents without human effort or domain-specific customization
- Current systems rely on simplifying assumptions and domain-specific training
- Researchers explore the use of large language models (LLMs) for generality in this task
- Prototype system called EVAPORATE powered by LLMs
- Two strategies: direct extraction using LLMs or code synthesis
- Tradeoff between cost and quality: code synthesis is cheaper but less accurate than direct extraction with LLMs
- Introduction of EVAPORATE-CODE+ improves quality by generating multiple candidate functions and ensembling their extractions using weak supervision
- EVAPORATE-CODE+ outperforms state-of-the-art systems and reduces LLM processing load by 110 times on average
- Focus on converting heterogeneous data sources into structured tables for analytical queries (webpages, PDFs, text documents)
- Example application: structuring attributes related to medical devices in FDA 510(k) reviews for premarket notification submissions
- LLM-based systems effectively generate structured views of heterogeneous data
Summary: Researchers are trying to create computer systems that can process documents without needing humans or specific customization. They are using large language models (LLMs) to make the systems more versatile. One prototype system called EVAPORATE uses LLMs to extract information directly or generate code. Code synthesis is cheaper but less accurate than direct extraction with LLMs. EVAPORATE-CODE+ improves quality by generating multiple candidate functions and combining their extractions. It outperforms other systems and reduces the processing load of LLMs.
Definitions- Automated systems: Computer programs that can do tasks without human help.
- Semi-structured documents: Documents that have some organization but not a strict format.
- Domain-specific customization: Making something specific to a certain field or area.
- Simplifying assumptions: Making things simpler by assuming certain things are true.
- Generality: Being able to work in many different situations or contexts.
- Prototype system: An early version of a system used for testing and development.
- Direct extraction: Taking information directly from a document without changing it.
- Code synthesis: Creating new code based on existing code or rules.
- Tradeoff: When you have to give up one thing in order to get another thing.
- Quality: How good something is at doing its job.
- Ensembling: Combining different things together into one group or set.
- Weak supervision: Giving some guidance or direction, but not full control or instruction.
- Heterogeneous data sources:
Exploring the Use of Large Language Models for Automated Data Extraction
Data extraction from semi-structured documents has been a long-standing goal in the data management community. However, due to the wide variety of potential documents, current systems often make simplifying assumptions and rely on domain-specific training. In this study, researchers explore whether large language models (LLMs) can help achieve generality in this task. The proposed system is called EVAPORATE and it is powered by LLMs.
Background
The study focuses on developing systems that can convert heterogeneous data sources into structured tables for analytical queries. These data sources include webpages, PDFs, and text documents. As an example application, medical researchers often analyze data from electronic health records (EHR), clinical trials, PubMed knowledge sources, and FDA reports. The researchers consider FDA 510(k) reviews for premarket notification submissions as a motivating setting. Their objective is to automatically structure attributes related to medical devices in these reviews and output a table format.
EVAPORATE System
The EVAPORATE system consists of two strategies for implementing automated document processing: directly extracting values from documents using LLMs or synthesizing code that performs the extraction. Through evaluations, they find that there is a tradeoff between cost and quality with these approaches; while code synthesis is cheaper it is less accurate compared to directly processing each document with LLMs. To improve the quality while maintaining low cost, the researchers introduce an extended code synthesis implementation called EVAPORATE-CODE+. This approach achieves better quality than direct extraction by generating multiple candidate functions and ensembling their extractions using weak supervision techniques such as majority voting or weighted averaging based on confidence scores assigned by the model during inference time..
Results
Overall, the study demonstrates that LLM-based systems can effectively generate structured views of heterogeneous data. The proposed EVAPORATE-CODE+ system achieves high-quality results while minimizing the processing load on the LLM; it reduces token processing by 110 times on average across various evaluation settings when compared to other state-of-the art systems .
Conclusion
This research paper provides evidence that large language models are effective tools for automating data extraction from semi-structured documents without relying heavily on domain specific training or customization efforts.. By introducing EVAPORATE-CODE+, which combines direct value extraction with code synthesis techniques using weak supervision methods such as majority voting or weighted averaging based on confidence scores assigned by the model during inference time., they have achieved higher accuracy levels at lower costs than other existing methods..
