# LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation

arXiv: https://arxiv.org/abs/2601.05192 · HTML: https://mlsanity.com/paper/2601.05192/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 78 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 30 of 52)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/1606.07947/) (2016)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs](https://mlsanity.com/paper/2505.00127/) (2025)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning](https://mlsanity.com/paper/2503.06034/) (2025)
- [Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases](https://mlsanity.com/paper/2009.11564/) (2020)
- [Multilingual Autoregressive Entity Linking](https://mlsanity.com/paper/2103.12528/) (2021)
- [TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy](https://mlsanity.com/paper/2406.11678/) (2024)
- [Neural Entity Linking: A Survey of Models Based on Deep Learning](https://mlsanity.com/paper/2006.00575/) (2020)
- [Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers](https://mlsanity.com/paper/2410.02642/) (2024)
- [ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability](https://mlsanity.com/paper/2508.07050/) (2025)
- [LongCodeZip: Compress Long Context for Code Language Models](https://mlsanity.com/paper/2510.00446/) (2025)
- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [Fine-Grained Entity Typing for Domain Independent Entity Linking](https://mlsanity.com/paper/1909.05780/) (2019)
- [Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching](https://mlsanity.com/paper/2405.16884/) (2024)
- [Named Entity Disambiguation for Noisy Text](https://mlsanity.com/paper/1706.09147/) (2017)
- [jina-reranker-v3: Last but Not Late Interaction for Listwise Document Reranking](https://mlsanity.com/paper/2509.25085/) (2025)
- [Highly Parallel Autoregressive Entity Linking with Discriminative Correction](https://mlsanity.com/paper/2109.03792/) (2021)
- [LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking](https://mlsanity.com/paper/2407.04020/) (2024)

## Built on by (most cited first, 6 of 6)

- [BeLink: Biomedical Entity Linking Meets Generative Re-Ranking](https://mlsanity.com/paper/2605.22501/) (2026)
- [LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation](https://mlsanity.com/paper/2605.26956/) (2026)
- [KARLA: Knowledge-base Augmented Retrieval for Language Models](https://mlsanity.com/paper/2606.26807/) (2026)
- [GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base](https://mlsanity.com/paper/2608.06992/) (2026)
- [Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection](https://mlsanity.com/paper/2608.27470/) (2026)
- [Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking](https://mlsanity.com/paper/2609.10745/) (2026)

## Abstract (arXiv)

Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundational step in tasks such as knowledge graph construction, question-answering, and information extraction. Our method, LELA, is a modular coarse-to-fine approach that leverages the capabilities of large language models (LLMs), and works with different target domains, knowledge bases and LLMs, without any fine-tuning phase. Our experiments across various entity linking settings show that LELA is highly competitive with fine-tuned approaches, and substantially outperforms the non-fine-tuned ones.