# From Retrieval to Generation: Efficient and Effective Entity Set Expansion

arXiv: https://arxiv.org/abs/2304.03531 · HTML: https://mlsanity.com/paper/2304.03531/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 62 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Are we ready for a new paradigm shift? A Survey on Visual Deep MLP](https://mlsanity.com/paper/2111.04060/) (2021)
- [The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking](https://mlsanity.com/paper/2203.00991/) (2022)
- [Neural Entity Linking: A Survey of Models Based on Deep Learning](https://mlsanity.com/paper/2006.00575/) (2020)
- [TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network](https://mlsanity.com/paper/2001.09522/) (2020)
- [Contrastive Learning with Hard Negative Entities for Entity Set Expansion](https://mlsanity.com/paper/2204.07789/) (2022)
- [SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble](https://mlsanity.com/paper/1910.08192/) (2019)
- [Empower Entity Set Expansion via Language Model Probing](https://mlsanity.com/paper/2004.13897/) (2020)
- [Automatic Context Pattern Generation for Entity Set Expansion](https://mlsanity.com/paper/2207.08087/) (2022)
- [EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1801.03825/) (2018)
- [Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment](https://mlsanity.com/paper/2302.08774/) (2023)
- [Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion](https://mlsanity.com/paper/2001.10106/) (2020)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge](https://mlsanity.com/paper/2211.10997/) (2022)
- [Term Set Expansion based NLP Architect by Intel AI Lab](https://mlsanity.com/paper/1808.08953/) (2018)
- [SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery](https://mlsanity.com/paper/2009.13827/) (2020)
- [Active Relation Discovery: Towards General and Label-aware Open Relation Extraction](https://mlsanity.com/paper/2211.04215/) (2022)
- [A Two-Stage Masked LM Method for Term Set Expansion](https://mlsanity.com/paper/2005.01063/) (2020)
- [A non-hierarchical attention network with modality dropout for textual response generation in multimodal dialogue systems](https://mlsanity.com/paper/2110.09702/) (2021)
- [Lightly-supervised Representation Learning with Global Interpretability](https://mlsanity.com/paper/1805.11545/) (2018)
- [Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation](https://mlsanity.com/paper/2210.16557/) (2022)
- [Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion](https://mlsanity.com/paper/2109.12082/) (2021)
- [Entity Synonym Discovery via Multipiece Bilateral Context Matching](https://mlsanity.com/paper/1901.00056/) (2018)

## Built on by (most cited first, 3 of 3)

- [MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities](https://mlsanity.com/paper/2307.14878/) (2023)
- [AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees](https://mlsanity.com/paper/2512.04550/) (2025)
- [Exploring the Implicit Semantic Ability of Multimodal Large Language Models: A Pilot Study on Entity Set Expansion](https://mlsanity.com/paper/2501.00330/) (2024)

## Abstract (arXiv)

Entity Set Expansion (ESE) is a critical task aiming at expanding entities of the target semantic class described by seed entities. Most existing ESE methods are retrieval-based frameworks that need to extract contextual features of entities and calculate the similarity between seed entities and candidate entities. To achieve the two purposes, they iteratively traverse the corpus and the entity vocabulary, resulting in poor efficiency and scalability. Experimental results indicate that the time consumed by the retrieval-based ESE methods increases linearly with entity vocabulary and corpus size. In this paper, we firstly propose Generative Entity Set Expansion (GenExpan) framework, which utilizes a generative pre-trained auto-regressive language model to accomplish ESE task. Specifically, a prefix tree is employed to guarantee the validity of entity generation, and automatically generated class names are adopted to guide the model to generate target entities. Moreover, we propose Knowledge Calibration and Generative Ranking to further bridge the gap between generic knowledge of the language model and the goal of ESE task. For efficiency, expansion time consumed by GenExpan is independent of entity vocabulary and corpus size, and GenExpan achieves an average 600% speedup compared to strong baselines. For expansion effectiveness, our framework outperforms previous state-of-the-art ESE methods.