# Variational Inference for Learning Representations of Natural Language Edits

arXiv: https://arxiv.org/abs/2004.09143 · HTML: https://mlsanity.com/paper/2004.09143/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Aligning LLM Agents by Learning Latent Preference from User Edits](https://mlsanity.com/paper/2404.15269/) (2024)
- [DiffusER: Discrete Diffusion via Edit-based Reconstruction](https://mlsanity.com/paper/2210.16886/) (2022)
- [Learning to Model Editing Processes](https://mlsanity.com/paper/2205.12374/) (2022)
- [Analyzing the Evolution of Graphs and Texts](https://mlsanity.com/paper/2411.06295/) (2024)
- [Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward](https://mlsanity.com/paper/2601.19055/) (2026)

## Abstract (arXiv)

Document editing has become a pervasive component of the production of information, with version control systems enabling edits to be efficiently stored and applied. In light of this, the task of learning distributed representations of edits has been recently proposed. With this in mind, we propose a novel approach that employs variational inference to learn a continuous latent space of vector representations to capture the underlying semantic information with regard to the document editing process. We achieve this by introducing a latent variable to explicitly model the aforementioned features. This latent variable is then combined with a document representation to guide the generation of an edited version of this document. Additionally, to facilitate standardized automatic evaluation of edit representations, which has heavily relied on direct human input thus far, we also propose a suite of downstream tasks, PEER, specifically designed to measure the quality of edit representations in the context of natural language processing.