Tackling challenges in large language model-based data extraction via context engineering: A commentary on Jansen et al. (2025)
Abstract
Systematic reviews, particularly meta-analyses, involve crucial yet labor-intensive and error-prone stages of data extraction. Recent advances in large language models (LLMs) have unlocked new avenues for automating this process, potentially enhancing both efficiency and reliability. Recently, Jansen et al. (2025) systematically evaluated the accuracy and error patterns of LLM-assisted data extraction across 22 reviews published in Psychological Bulletin. Their findings indicated that while achieving acceptable-to-good accuracy for some variables describing study characteristics, LLMs struggled with numerical variables, especially those related to effect sizes. In this commentary, we discuss the current challenges of automated data extraction and potential pathways to improve the work reported in Jansen et al.'s study. We situate our discussion within the framework of context engineering, aiming to refine the information provided to LLMs through dynamic optimization strategies tailored to specific tasks. We identify five key challenges that reflect either LLMs' unique patterns or standard practices in research synthesis: parsing semistructured data, understanding long contexts, performing arithmetic induction, engaging in complex reasoning, and ensuring the reproducibility of coding protocols. We then outline potential solutions inspired by context engineering implementations such as retrieval-augmented generation and tool-integrated reasoning. For illustration, we present…
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