Visual Analysis of LLM-based Entity Resolution from Scientific Papers

• URL: http://arxiv.org/abs/2609.06037v1
• Date: Sat, 05 Sep 2026 11:42:36 GMT
• Title: Visual Analysis of LLM-based Entity Resolution from Scientific Papers
• Abstract summary: This paper focuses on the visual analytics support for extracting domain-specific entity from scientific literature.<n>We propose human-in-the-loop refinement over the entity resolution process using visual analytics techniques.
• Score: 20.041142474716853
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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