Leveraging the Power of Large Language Models in Entity Linking via Adaptive Routing and Targeted Reasoning

• URL: http://arxiv.org/abs/2510.20098v1
• Date: Thu, 23 Oct 2025 00:50:14 GMT
• Title: Leveraging the Power of Large Language Models in Entity Linking via Adaptive Routing and Targeted Reasoning
• Authors: Yajie Li, Albert Galimov, Mitra Datta Ganapaneni, Pujitha Thejaswi, De Meng, Priyanshu Kumar, Saloni Potdar,
• Abstract summary: ARTER presents a structured pipeline that achieves high performance without deep fine-tuning.<n>It strategically combines candidate generation, context-based scoring, adaptive routing, and selective reasoning.<n>On standard benchmarks, ARTER outperforms ReFinED by up to +4.47%, with an average gain of +2.53% on 5 out of 6 datasets.
• Score: 4.338036373287262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity Linking (EL) has traditionally relied on large annotated datasets and extensive model fine-tuning. While recent few-shot methods leverage large language models (LLMs) through prompting to reduce training requirements, they often suffer from inefficiencies due to expensive LLM-based reasoning. ARTER (Adaptive Routing and Targeted Entity Reasoning) presents a structured pipeline that achieves high performance without deep fine-tuning by strategically combining candidate generation, context-based scoring, adaptive routing, and selective reasoning. ARTER computes a small set of complementary signals(both embedding and LLM-based) over the retrieved candidates to categorize contextual mentions into easy and hard cases. The cases are then handled by a low-computational entity linker (e.g. ReFinED) and more expensive targeted LLM-based reasoning respectively. On standard benchmarks, ARTER outperforms ReFinED by up to +4.47%, with an average gain of +2.53% on 5 out of 6 datasets, and performs comparably to pipelines using LLM-based reasoning for all mentions, while being as twice as efficient in terms of the number of LLM tokens.

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