---
title: "Learning to Edit: Aligning LLMs with Knowledge Editing (2024)"
description: "They propose Learning to Edit (LTE), which teaches the model how to use supplied updates rather… 44 references, cited by 27 papers here."
authors: ["Yuxin Jiang", "Yufei Wang", "Chuhan Wu", "Wanjun Zhong", "Xingshan Zeng", "Jiahui Gao", "Liangyou Li", "Xin Jiang", "Lifeng Shang", "Ruiming Tang", "Qun Liu", "Wei Wang"]
year: 2024
venue: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2024.acl-long.258/"
doi: "10.18653/v1/2024.acl-long.258"
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/jiang-2024-learning-edit-aligning-llms-knowledge/"
---
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# Learning to Edit: Aligning LLMs with Knowledge Editing

**Authors:** [Yuxin Jiang](https://md.research.elamur.ai/free/en/authors/yuxin-jiang/), [Yufei Wang](https://md.research.elamur.ai/free/en/authors/yufei-wang/), [Chuhan Wu](https://md.research.elamur.ai/free/en/authors/chuhan-wu/), [Wanjun Zhong](https://md.research.elamur.ai/free/en/authors/wanjun-zhong/), [Xingshan Zeng](https://md.research.elamur.ai/free/en/authors/xingshan-zeng/), [Jiahui Gao](https://md.research.elamur.ai/free/en/authors/jiahui-gao/), [Liangyou Li](https://md.research.elamur.ai/free/en/authors/liangyou-li/), [Xin Jiang](https://md.research.elamur.ai/free/en/authors/xin-jiang/), [Lifeng Shang](https://md.research.elamur.ai/free/en/authors/lifeng-shang/), [Ruiming Tang](https://md.research.elamur.ai/free/en/authors/ruiming-tang/), [Qun Liu](https://md.research.elamur.ai/free/en/authors/qun-liu/) and [Wei Wang](https://md.research.elamur.ai/free/en/authors/wei-wang/) · **Year:** 2024 · **Venue:** [Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2024](https://md.research.elamur.ai/free/en/venues/acl/2024/)) · **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/2024.acl-long.258/) · [PDF](https://aclanthology.org/2024.acl-long.258.pdf) · [DOI 10.18653/v1/2024.acl-long.258](https://doi.org/10.18653/v1/2024.acl-long.258) · [OpenAlex](https://openalex.org/W4402671939)

**Citations:** 12 citations (OpenAlex, 2026-10-14) · [27](https://md.research.elamur.ai/free/en/papers/jiang-2024-learning-edit-aligning-llms-knowledge/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Knowledge editing tries to change a small set of facts inside an LLM without harming its behaviour on unrelated inputs. The authors argue that current editors (hypernetwork-based, scope-classifier, or locate-and-edit methods such as ROME and MEMIT) mostly make the model memorize new facts. As a result, the model struggles to reason with them alongside what it already knows. They propose Learning to Edit (LTE), which teaches the model how to use supplied updates rather than storing them.

In an Alignment Phase, the LLM is fine-tuned once on parallel data. Each edit descriptor is paired with in-scope and out-of-scope queries, shown both with and without an explicit editing prompt. The goal is reliable edits, preserved unrelated knowledge, and intact fluency. In an Inference Phase, relevant edits are retrieved from a memory bank, which enables real-time batch and sequential editing.

Across four benchmarks, two LLM architectures and seven baselines, LTE sets a new state of the art. It improves portability by more than 20 absolute points, degrades less under mass editing, and edits fastest. This makes it practical for keeping deployed models current.

## Key points
- **Training data:** LTE's alignment data are built only from the training splits of ZsRE, RIPPLEEDITS, WikiBio and MQUAKE, which avoids leakage. GPT-4 generated the out-of-scope queries that ZsRE and MQUAKE lack.
- **Prompt format:** Training inputs use an "[Updated Information] … [Query] …" template. At inference, retrieved edit descriptors are inserted into this template.
- **Evaluation metrics:** Edit success, portability, locality and fluency. Fluency is measured as a weighted average of bi-/tri-gram entropy.
- **Results:** LTE beats baselines such as SERAC, ICE, ROME, MEMIT and FT-L, with a portability gain of more than 20 points. It is also robust in batch and sequential editing and causes little interference on general tasks.
- **Limitations and resources:** Using LoRA instead of full fine-tuning keeps performance while saving resources. The work covers factual edits only; multilingual, multimodal and black-box editing are left open. Code and data: github.com/YJiangcm/LTE.

*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

- [Hong Kong University of Science and Technology](https://md.research.elamur.ai/free/en/universities/hong-kong-university-of-science-and-technology/) — University, Hong Kong, China · 142 works · 25,136 citations
- [The Hong Kong University of Science and Technology (Guangzhou)](https://md.research.elamur.ai/free/en/universities/the-hong-kong-university-of-science-and-technology-guangzhou/) — University, Guangzhou, China · 53 works · 1,367 citations

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## Cite

Cite as: Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu and Wei Wang. 2024. Learning to Edit: Aligning LLMs with Knowledge Editing. Proceedings of the 62nd 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{jiang2024learning,
  title = {{Learning to Edit: Aligning LLMs with Knowledge Editing}},
  author = {Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu and Wei Wang},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  doi = {10.18653/v1/2024.acl-long.258},
  url = {https://doi.org/10.18653/v1/2024.acl-long.258}
}
```

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*Updated 30 October 2026*