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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Tackling challenges in large language model-based data extraction via context engineering: A commentary on Jansen et
Junsong Lu1, X T XiaoTian Wang2
1University of California-San Diego, Department of Psychology.
Psychological Bulletin
|July 9, 2026
Summary
Large language models (LLMs) show promise for automating data extraction in systematic reviews but struggle with numerical data. Context engineering strategies can enhance LLM accuracy for research synthesis tasks.
Area of Science:
- Systematic Reviews & Meta-Analyses
- Artificial Intelligence in Research
Background:
- Data extraction in systematic reviews is labor-intensive and prone to errors.
- Large language models (LLMs) offer potential for automating this process, improving efficiency and reliability.
- Jansen et al. (2025) evaluated LLM accuracy in data extraction, finding success with study characteristics but limitations with numerical data like effect sizes.
Purpose of the Study:
- To discuss current challenges in automated data extraction using LLMs.
- To propose context engineering strategies for improving LLM performance in research synthesis.
- To identify pathways for advancing LLM-assisted data extraction beyond basic instruction following.
Main Methods:
- Analysis of challenges in LLM-assisted data extraction, including parsing semistructured data, long contexts, arithmetic induction, complex reasoning, and reproducibility.
- Exploration of context engineering solutions like retrieval-augmented generation and tool-integrated reasoning.
- Illustration of solutions with examples: OCR for semistructured data, function calls for effect sizes, LLM agents for retrieval, and self-refinement for output improvement.
Main Results:
- LLMs demonstrate acceptable-to-good accuracy for study characteristic variables but struggle with numerical variables, particularly effect sizes.
- Five key challenges were identified: parsing semistructured data, understanding long contexts, arithmetic induction, complex reasoning, and ensuring reproducibility.
- Proposed solutions include retrieval-augmented generation, tool-integrated reasoning, OCR, function calls, LLM agents, and self-refinement.
Conclusions:
- Automated data extraction using LLMs faces significant challenges, especially with numerical and complex data.
- Context engineering offers a promising framework for enhancing LLM accuracy and reliability in research synthesis.
- Future research should focus on developing advanced context engineering techniques for more robust automated data extraction.
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