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

arXiv: https://arxiv.org/abs/2510.20098 · HTML: https://mlsanity.com/paper/2510.20098/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Contrastive Chain-of-Thought Prompting](https://mlsanity.com/paper/2311.09277/) (2023)
- [LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking](https://mlsanity.com/paper/2407.04020/) (2024)
- [Entity Disambiguation via Fusion Entity Decoding](https://mlsanity.com/paper/2404.01626/) (2024)

## Built on by (most cited first, 4 of 4)

- [LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation](https://mlsanity.com/paper/2601.05192/) (2026)
- [TELLER: Dual-Path Iterative Preference Optimization for Table Entity Linking](https://mlsanity.com/paper/2607.28680/) (2026)
- [Query-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation](https://mlsanity.com/paper/2608.25922/) (2026)
- [Beyond Single-Negative Preference: Multi-Negative DPO for LLM-Centric Historical Entity Linking](https://mlsanity.com/paper/2609.07379/) (2026)

## Abstract (arXiv)

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.