---
title: "Who cites KnowCoder: Coding Structured — 37 citing papers"
description: "37 papers of the Elamur Research library cite «KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction»…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [KnowCoder: Coding Structured Knowledge into LLMs…](https://md.research.elamur.ai/free/en/papers/li-2024-knowcoder-coding-structured-knowledge-into/) / cited by

# Who cites «KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction»

37 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction»](https://md.research.elamur.ai/free/en/papers/li-2024-knowcoder-coding-structured-knowledge-into/)

1. **[Large Language Models for Generative Information Extraction: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2023-large-language-models-generative-information/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Derong Xu, Wei Chen, Wenjun Peng, et al.*2023 · 406 citations*
2. [**Constituency Parsing using LLMs arXiv:2310.19462**Xuefeng Bai, Jialong Wu, Yulong Chen, et al.*2023 · 14 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-constituency-parsing-using-llms/)
3. **[Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/yang-2025-beyond-boundaries-learning-universal-entity/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yuming Yang, Wantong Zhao, Caishuang Huang, et al.*2025 · 11 citations*
4. **[Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations](https://md.research.elamur.ai/free/en/papers/mtumbuka-2026-contrastively-pre-trained-event-embeddings/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Frank Mtumbuka, Steven Schockaert*2026*
5. **[EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain](https://md.research.elamur.ai/free/en/papers/lu-2026-exceeds-extracting-complex-events-nugget/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yi-Fan Lu, Xian-Ling Mao, Bo Wang, et al.*2026*
6. **[ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction](https://md.research.elamur.ai/free/en/papers/liu-2026-prouie-macro-micro-progressive-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenda Liu, Song Zhigang, Shuai Nie, et al.*2026*

31 more papers of our library cite it; they have no page here yet.

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