# ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget

arXiv: https://arxiv.org/abs/2408.00103 · HTML: https://mlsanity.com/paper/2408.00103/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 58 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 30 of 33)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification](https://mlsanity.com/paper/2010.13641/) (2020)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [Joint Entity and Relation Extraction with Set Prediction Networks](https://mlsanity.com/paper/2011.01675/) (2020)
- [GenIE: Generative Information Extraction](https://mlsanity.com/paper/2112.08340/) (2021)
- [Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking](https://mlsanity.com/paper/2003.05473/) (2020)
- [Universal Information Extraction as Unified Semantic Matching](https://mlsanity.com/paper/2301.03282/) (2023)
- [PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction](https://mlsanity.com/paper/2106.09895/) (2021)
- [EntQA: Entity Linking as Question Answering](https://mlsanity.com/paper/2110.02369/) (2021)
- [Joint Learning of Named Entity Recognition and Entity Linking](https://mlsanity.com/paper/1907.08243/) (2019)
- [Highly Parallel Autoregressive Entity Linking with Discriminative Correction](https://mlsanity.com/paper/2109.03792/) (2021)
- [Analysis of Named Entity Recognition and Linking for Tweets](https://mlsanity.com/paper/1410.7182/) (2014)
- [Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization](https://mlsanity.com/paper/2204.13761/) (2022)
- [A Survey for Efficient Open Domain Question Answering](https://mlsanity.com/paper/2211.07886/) (2022)
- [Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment](https://mlsanity.com/paper/2302.08774/) (2023)
- [Word-Entity Duet Representations for Document Ranking](https://mlsanity.com/paper/1706.06636/) (2017)
- [TempEL: Linking Dynamically Evolving and Newly Emerging Entities](https://mlsanity.com/paper/2302.02500/) (2023)

## Built on by (most cited first, 16 of 16)

- [Evaluating Design Decisions for Dual Encoder-based Entity Disambiguation](https://mlsanity.com/paper/2505.11683/) (2025)
- [BOOKCOREF: Coreference Resolution at Book Scale](https://mlsanity.com/paper/2507.12075/) (2025)
- [Beyond the Numbers: Transparency in Relation Extraction Benchmark Creation and Leaderboards](https://mlsanity.com/paper/2411.05224/) (2024)
- [G-RAG: Knowledge Expansion in Material Science](https://mlsanity.com/paper/2411.14592/) (2024)
- [Musical Heritage Historical Entity Linking](https://mlsanity.com/paper/2502.09168/) (2025)
- [Evaluation of LLMs on Long-tail Entity Linking in Historical Documents](https://mlsanity.com/paper/2505.03473/) (2025)
- [An Entity Linking Agent for Question Answering](https://mlsanity.com/paper/2508.03865/) (2025)
- [Relation Extraction or Pattern Matching? Unravelling the Generalisation Limits of Language Models for Biographical RE](https://mlsanity.com/paper/2505.12533/) (2025)
- [LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation](https://mlsanity.com/paper/2605.26956/) (2026)
- [Autoregressive Language Models for Knowledge Base Population: A case study in the space mission domain](https://mlsanity.com/paper/2503.18502/) (2025)
- [EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledge](https://mlsanity.com/paper/2507.03617/) (2025)
- [Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms](https://mlsanity.com/paper/2512.05967/) (2025)
- [TailNLG: A Multilingual Benchmark Addressing Verbalization of Long-Tail Entities](https://mlsanity.com/paper/2603.27768/) (2026)
- [BioELX: Context-Aware Cross-lingual Biomedical Entity Linking without Task-Specific Supervision](https://mlsanity.com/paper/2605.27380/) (2026)
- [The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes](https://mlsanity.com/paper/2606.11470/) (2026)
- [Query Implied Generative Engine Optimization](https://mlsanity.com/paper/2609.27845/) (2026)

## Abstract (arXiv)

Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper, we propose ReLiK, a Retriever-Reader architecture for both EL and RE, where, given an input text, the Retriever module undertakes the identification of candidate entities or relations that could potentially appear within the text. Subsequently, the Reader module is tasked to discern the pertinent retrieved entities or relations and establish their alignment with the corresponding textual spans. Notably, we put forward an innovative input representation that incorporates the candidate entities or relations alongside the text, making it possible to link entities or extract relations in a single forward pass and to fully leverage pre-trained language models contextualization capabilities, in contrast with previous Retriever-Reader-based methods, which require a forward pass for each candidate. Our formulation of EL and RE achieves state-of-the-art performance in both in-domain and out-of-domain benchmarks while using academic budget training and with up to 40x inference speed compared to competitors. Finally, we show how our architecture can be used seamlessly for Information Extraction (cIE), i.e. EL + RE, and setting a new state of the art by employing a shared Reader that simultaneously extracts entities and relations.