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Retrieval-Augmented Code Generation for Universal Information Extraction
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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.
Forward citations
Cited by 6 Pith papers
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LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction
Label-aware diagnostic reflection plus two-stage outcome GRPO improves same-backbone IE F1 over SFT, with larger gains under relation-extraction domain shift.
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MPL: Multiple Programming Languages with Large Language Models for Information Extraction
Using multiple programming languages as code-style prompts during fine-tuning improves LLM information extraction accuracy over single-language prompting.
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Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA
Tea-MOELORA uses separate task and era gates over LoRA experts to jointly train relation and event extraction across classical and modern Chinese, improving F1 over joint LoRA and existing LoRA-MoE baselines on most datasets.
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SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Code retrievers rely on surface text like identifiers and docstrings rather than code semantics, and a description-based reranking pipeline, SACL, compensates for that bias but its headline gains come mostly from arti...
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KnowCoder-V2: Deep Knowledge Analysis
KnowCoder-V2 augments deep research with offline knowledge organization and code-based knowledge computation, reporting gains on information extraction, KBQA, and LLM-judged report generation.
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Schema as Parameterized Tools for Universal Information Extraction
SPT turns predefined IE schemas into trainable token embeddings, enabling one small model to handle closed, open, and on-demand extraction through schema retrieval, generation, and infilling.
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