# On Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approach

arXiv: https://arxiv.org/abs/2401.03426 · HTML: https://mlsanity.com/paper/2401.03426/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 7 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Large Language Models as Data Preprocessors](https://mlsanity.com/paper/2308.16361/) (2023)
- [Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]](https://mlsanity.com/paper/2304.12329/) (2023)
- [End-to-End Entity Resolution for Big Data: A Survey](https://mlsanity.com/paper/1905.06397/) (2019)

## Built on by (most cited first, 5 of 5)

- [CaRL-EM: Cost-Aware Reinforcement Learning for Entity Matching with LLMs](https://mlsanity.com/paper/2609.01195/) (2026)
- [EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detection](https://mlsanity.com/paper/2512.11506/) (2025)
- [OpenSanctions Pairs: Large-Scale Entity Matching with LLMs](https://mlsanity.com/paper/2603.11051/) (2026)
- [Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts](https://mlsanity.com/paper/2605.23597/) (2026)
- [Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution](https://mlsanity.com/paper/2605.25814/) (2026)

## Abstract (arXiv)

Entity resolution, the task of identifying and merging records that refer to the same real-world entity, is crucial in sectors like e-commerce, healthcare, and law enforcement. Large Language Models (LLMs) introduce an innovative approach to this task, capitalizing on their advanced linguistic capabilities and a ``pay-as-you-go'' model that provides significant advantages to those without extensive data science expertise. However, current LLMs are costly due to per-API request billing. Existing methods often either lack quality or become prohibitively expensive at scale. To address these problems, we propose an uncertainty reduction framework using LLMs to improve entity resolution results. We first initialize possible partitions of the entity cluster, refer to the same entity, and define the uncertainty of the result. Then, we reduce the uncertainty by selecting a few valuable matching questions for LLM verification. Upon receiving the answers, we update the probability distribution of the possible partitions. To further reduce costs, we design an efficient algorithm to judiciously select the most valuable matching pairs to query. Additionally, we create error-tolerant techniques to handle LLM mistakes and a dynamic adjustment method to reach truly correct partitions. Experimental results show that our method is efficient and effective, offering promising applications in real-world tasks.