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arXiv · 2609.06037

Visual Analysis of LLM-based Entity Resolution from Scientific Papers

Siyu Wu·Yi Yang·Weize Wu·Ruiming Li·Yuyang Zhang·Ge Wang·Huobin Tan·Zipeng Liu·Lei Shi
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Abstract

This paper focuses on the visual analytics support for extracting domain-specific entity from extensive scientific literature, a task with inherent limitations using traditional named entity resolution methods. With the advent of large language models (LLMs) such as GPT-4, significant improvements over conventional machine learning approaches have been achieved due to LLM's capability on entity resolution integrate abilities such as understanding multiple types of text. This research introduces a new visual analysis pipeline that integrates these advanced LLMs with versatile visualization and interaction designs to support batch entity resolution. Specifically, we focus on a specific material science field of Metal-Organic Frameworks (MOFs) and a large data collection namely CSD-MOFs. Through collaboration with domain experts in material science, we obtain well-labeled synthesis paragraphs. We propose human-in-the-loop refinement over the entity resolution process using visual analytics techniques, which allows domain experts to interactively integrate insights into LLM intelligence, including error analysis and interpretation of the retrieval-augmented generation (RAG) algorithm. Our evaluation through the case study of example selection for RAG demonstrates that this human-machine collaborative approach improved single-document entity resolution accuracy by approximately 30%.

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Siyu Wu, Yi Yang, Weize Wu, Ruiming Li, Yuyang Zhang, Ge Wang, Huobin Tan, Zipeng Liu, Lei Shi. 2026-09-05. Visual Analysis of LLM-based Entity Resolution from Scientific Papers. https://doi.org/10.1016/j.visinf.2025.100236

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