# Retrieval-Augmented Code Generation for Universal Information Extraction arXiv: https://arxiv.org/abs/2311.02962 ยท HTML: https://mlsanity.com/paper/2311.02962/ ## Key facts - Cited by 40 papers in the map (data through 14 Oct 2026). - Placed in RAG for code, one of 15 directions in Retrieval-augmented generation. - Builds on 18 of its 31 references that are in the map. - First posted on arXiv 6 Nov 2023. ## Placed in - Retrieval-augmented generation / [RAG for code](https://mlsanity.com/rag/rag-for-code/) ## Builds on (most cited first, 18 of 18) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020) - [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018) - [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022) - [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023) - [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023) - [Revisiting Relation Extraction in the era of Large Language Models](https://mlsanity.com/paper/2305.05003/) (2023) - [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020) - [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021) - [Code4Struct: Code Generation for Few-Shot Event Structure Prediction](https://mlsanity.com/paper/2210.12810/) (2022) - [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023) - [Packed Levitated Marker for Entity and Relation Extraction](https://mlsanity.com/paper/2109.06067/) (2021) - [Universal Information Extraction as Unified Semantic Matching](https://mlsanity.com/paper/2301.03282/) (2023) - [FLERT: Document-Level Features for Named Entity Recognition](https://mlsanity.com/paper/2011.06993/) (2020) - [Assertion-based QA with Question-Aware Open Information Extraction](https://mlsanity.com/paper/1801.07414/) (2018) - [Intelligent Online Selling Point Extraction for E-Commerce Recommendation](https://mlsanity.com/paper/2112.10613/) (2021) ## Built on by (most cited first, 30 of 40) - [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024) - [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023) - [RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation](https://mlsanity.com/paper/2408.08067/) (2024) - [VideoRAG: Retrieval-Augmented Generation over Video Corpus](https://mlsanity.com/paper/2501.05874/) (2025) - [KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction](https://mlsanity.com/paper/2403.07969/) (2024) - [ADELIE: Aligning Large Language Models on Information Extraction](https://mlsanity.com/paper/2405.05008/) (2024) - [C-ICL: Contrastive In-context Learning for Information Extraction](https://mlsanity.com/paper/2402.11254/) (2024) - [EVOR: Evolving Retrieval for Code Generation](https://mlsanity.com/paper/2402.12317/) (2024) - [Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines](https://mlsanity.com/paper/2502.16377/) (2025) - [A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction](https://mlsanity.com/paper/2402.13364/) (2024) - [Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction](https://mlsanity.com/paper/2406.12197/) (2024) - [LTNER: Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking](https://mlsanity.com/paper/2404.05624/) (2024) - [Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.08690/) (2025) - [GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect](https://mlsanity.com/paper/2311.06838/) (2023) - [AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction](https://mlsanity.com/paper/2409.01854/) (2024) - [Building A Coding Assistant via the Retrieval-Augmented Language Model](https://mlsanity.com/paper/2410.16229/) (2024) - [Retrieval Augmented Instruction Tuning for Open NER with Large Language Models](https://mlsanity.com/paper/2406.17305/) (2024) - [SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization](https://mlsanity.com/paper/2506.20081/) (2025) - [EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention](https://mlsanity.com/paper/2508.16771/) (2025) - [Embedded Named Entity Recognition using Probing Classifiers](https://mlsanity.com/paper/2403.11747/) (2024) - [KnowCoder-X: Boosting Multilingual Information Extraction via Code](https://mlsanity.com/paper/2411.04794/) (2024) - [Schema as Parameterized Tools for Universal Information Extraction](https://mlsanity.com/paper/2506.01276/) (2025) - [Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction](https://mlsanity.com/paper/2503.02628/) (2025) - [MPL: Multiple Programming Languages with Large Language Models for Information Extraction](https://mlsanity.com/paper/2505.16107/) (2025) - [RA-Gen: A Controllable Code Generation Framework Using ReAct for Multi-Agent Task Execution](https://mlsanity.com/paper/2510.08665/) (2025) - [Extracting Events Like Code: A Multi-Agent Programming Framework for Zero-Shot Event Extraction](https://mlsanity.com/paper/2511.13118/) (2025) - [Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases](https://mlsanity.com/paper/2403.16396/) (2024) - [Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier](https://mlsanity.com/paper/2501.05155/) (2025) - [KnowCoder-V2: Deep Knowledge Analysis](https://mlsanity.com/paper/2506.06881/) (2025) - [A Semantic Parsing Framework for End-to-End Time Normalization](https://mlsanity.com/paper/2507.06450/) (2025) ## Abstract (arXiv) Information Extraction (IE) aims to extract structural knowledge (e.g., entities, relations, events) from natural language texts, which brings challenges to existing methods due to task-specific schemas and complex text expressions. Code, as a typical kind of formalized language, is capable of describing structural knowledge under various schemas in a universal way. On the other hand, Large Language Models (LLMs) trained on both codes and texts have demonstrated powerful capabilities of transforming texts into codes, which provides a feasible solution to IE tasks. Therefore, in this paper, we propose a universal retrieval-augmented code generation framework based on LLMs, called Code4UIE, for IE tasks. Specifically, Code4UIE adopts Python classes to define task-specific schemas of various structural knowledge in a universal way. By so doing, extracting knowledge under these schemas can be transformed into generating codes that instantiate the predefined Python classes with the information in texts. To generate these codes more precisely, Code4UIE adopts the in-context learning mechanism to instruct LLMs with examples. In order to obtain appropriate examples for different tasks, Code4UIE explores several example retrieval strategies, which can retrieve examples semantically similar to the given texts. Extensive experiments on five representative IE tasks across nine datasets demonstrate the effectiveness of the Code4UIE framework.