---
title: "CodeIE — paper by Li et al. (2023) — summary and ideas"
description: "Information extraction covers tasks like recognizing named entities (NER) and extracting relations… 33 references, cited by 55 papers here."
authors: ["Peng Li", "Tianxiang Sun", "Qiong Tang", "Hang Yan", "Yuanbin Wu", "Xuanjing Huang", "Xipeng Qiu"]
year: 2023
venue: "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2023.acl-long.855/"
doi: "10.18653/v1/2023.acl-long.855"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2023-codeie-large-code-generation-models/"
---
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# CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors

**Authors:** [Peng Li](https://md.research.elamur.ai/free/en/authors/peng-li/), [Tianxiang Sun](https://md.research.elamur.ai/free/en/authors/tianxiang-sun/), [Qiong Tang](https://md.research.elamur.ai/free/en/authors/qiong-tang/), [Hang Yan](https://md.research.elamur.ai/free/en/authors/hang-yan/), [Yuanbin Wu](https://md.research.elamur.ai/free/en/authors/yuanbin-wu/), [Xuanjing Huang](https://md.research.elamur.ai/free/en/authors/xuanjing-huang/) and [Xipeng Qiu](https://md.research.elamur.ai/free/en/authors/xipeng-qiu/) · **Year:** 2023 · **Venue:** [Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2023](https://md.research.elamur.ai/free/en/venues/acl/2023/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.acl-long.855/) · [PDF](https://aclanthology.org/2023.acl-long.855.pdf) · [DOI 10.18653/v1/2023.acl-long.855](https://doi.org/10.18653/v1/2023.acl-long.855) · [OpenAlex](https://openalex.org/W4385572217)

**Citations:** 54 citations (OpenAlex, 2026-10-14) · [55](https://md.research.elamur.ai/free/en/papers/li-2023-codeie-large-code-generation-models/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Information extraction covers tasks like recognizing named entities (NER) and extracting relations between them (RE), both of which yield structured outputs. These are awkward to express as plain text, so prompting natural-language LLMs (NL-LLMs) like GPT-3 tends to yield fragile, malformed predictions. Moderate-size generative IE models like UIE also degrade sharply when only a few training examples are available.

CodeIE reframes both tasks as Python code completion:
- The input sentence is placed inside a function with a descriptive name and docstring.
- An empty list is initialized for the results.
- Each entity or relation is added through `append` calls containing dictionaries.

A handful of labeled examples in this format serve as in-context demonstrations for a code model (Codex), with no fine-tuning.

Across seven NER and RE benchmarks in few-shot settings, this approach consistently beats fine-tuned UIE and text-prompted GPT-3. Code prompts help both model families, and Codex outperforms GPT-3 regardless of prompt style. The work shows that matching the output format to what a model saw during pre-training makes structured extraction more reliable.

## Key points
- **Output format:** NER targets are dictionaries with keys `text` and `type`. RE targets use `rel_type`, `ent1_type`, `ent1_text`, `ent2_type` and `ent2_text`.
- **Motivation:** with 5-shot training on CoNLL03 NER, UIE loses roughly 20% of its full-data performance.
- **Structural errors:** code-style prompts produce almost no structurally invalid outputs, while text prompts often do. Codex also makes fewer semantic violations than GPT-3, such as inventing entity types outside the defined set or extracting spans absent from the input.
- **Precision and recall:** code prompts raise both. Codex gains mainly in recall on NER and improves both metrics on RE.
- **Limitations and availability:** the study depends on closed, paid APIs (GPT-3, Codex) and is tested only on English data. Code is available at github.com/dasepli/CodeIE.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Fudan University](https://md.research.elamur.ai/free/en/universities/fudan-university/) — University, Shanghai, China · 65 works · 19,402 citations
- [East China Normal University](https://md.research.elamur.ai/free/en/universities/east-china-normal-university/) — University, Shanghai, China · 19 works · 1,856 citations

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🧭 Topics (1): [💻 Code & programming languages](https://md.research.elamur.ai/free/en/topics/code-and-programming/) · 56 works

## Cite

Cite as: Peng Li, Tianxiang Sun, Qiong Tang, Hang Yan, Yuanbin Wu, Xuanjing Huang and Xipeng Qiu. 2023. CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{li2023codeie,
  title = {{CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors}},
  author = {Peng Li, Tianxiang Sun, Qiong Tang, Hang Yan, Yuanbin Wu, Xuanjing Huang and Xipeng Qiu},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-long.855},
  url = {https://doi.org/10.18653/v1/2023.acl-long.855}
}
```

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*Updated 24 October 2026*