Scientific progress is driven by the deliberate articulation of what remains unknown. This study investigates the ability of large language models (LLMs) to identify research knowledge gaps in the biomedical literature. We define two categories of knowledge gaps: explicit gaps, clear declarations of missing knowledge; and implicit gaps, context-inferred missing knowledge. While prior work has focused mainly on explicit gap detection, we extend this line of research by addressing the novel task of inferring implicit gaps. We conducted two experiments on almost 1500 documents across four datasets, including a manually annotated corpus of biomedical articles. We benchmarked both closed-weight models (from OpenAI) and open-weight models (Llama and Gemma 2) under paragraph-level and full-paper settings. To address the reasoning of implicit gaps inference, we introduce \textbf{\small TABI}, a Toulmin-Abductive Bucketed Inference scheme that structures reasoning and buckets inferred conclusion candidates for validation. Our results highlight the robust capability of LLMs in identifying both explicit and implicit knowledge gaps. This is true for both open- and closed-weight models, with larger variants often performing better. This suggests a strong ability of LLMs for systematically identifying candidate knowledge gaps, which can support early-stage research formulation, policymakers, and funding decisions. We also report observed failure modes and outline directions for robust deployment, including domain adaptation, human-in-the-loop verification, and benchmarking across open- and closed-weight models.

翻译：科学进步源于对未知领域的明确阐述。本研究探讨了大型语言模型（LLMs）在识别生物医学文献中研究知识空白方面的能力。我们将知识空白定义为两类：显性空白（对缺失知识的明确声明）和隐性空白（通过上下文推断的缺失知识）。先前研究主要集中于显性空白的检测，而本研究通过解决推断隐性空白这一新任务，拓展了该研究方向。我们在四个数据集（包括一个手动标注的生物医学论文语料库）的近1500篇文献上进行了两项实验。我们在段落级和全文级设置下，对闭源权重模型（来自OpenAI）和开源权重模型（Llama与Gemma 2）进行了基准测试。为处理隐性空白推断的推理过程，我们提出了\\textbf{\\small TABI}（图尔敏-溯因分桶推理方案），该方案通过结构化推理将推断出的结论候选分桶以供验证。我们的结果表明，LLMs在识别显性和隐性知识空白方面均表现出稳健能力，开源与闭源权重模型均如此，且更大规模的模型通常表现更优。这揭示了LLMs在系统化识别候选知识空白方面的强大潜力，可支持早期研究规划、政策制定者及资助决策。我们还报告了观察到的失败模式，并提出了稳健部署的方向，包括领域适应、人机协同验证以及开源与闭源权重模型的基准测试。