Leveraging the Power of Large Language Models in Entity Linking via Adaptive Routing and Targeted Reasoning (2025.emnlp-industry)
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Yajie Li, Albert Galimov, Mitra Datta Ganapaneni, Pujitha Thejaswi, De Meng, Priyanshu Kumar, Saloni Potdar
| Challenge: | Entity Linking (EL) relies on large labeled datasets and extensive fine-tuning . lexical ambiguity, knowledge-intensive cases and low-context mentions are some of the challenges. |
| Approach: | Entity Linking (EL) relies on large annotated datasets and extensive fine-tuning . authors propose a pipeline that integrates candidate generation, context-based scoring, adaptive routing, and selective reasoning . |
| Outcome: | ARTER outperforms ReFinED and LLM-based pipelines on standard benchmarks . it performs twice as efficiently on 5 out of 6 datasets and doubles the number of tokens compared to pipelines using LLM . |
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