# kNN-BOX: A Unified Framework for Nearest Neighbor Generation

arXiv: https://arxiv.org/abs/2302.13574 · HTML: https://mlsanity.com/paper/2302.13574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?](https://mlsanity.com/paper/1804.06323/) (2018)
- [Neural CRF Model for Sentence Alignment in Text Simplification](https://mlsanity.com/paper/2005.02324/) (2020)
- [Efficient Nearest Neighbor Language Models](https://mlsanity.com/paper/2109.04212/) (2021)
- [Adaptive Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.13022/) (2021)
- [Quasar: Datasets for Question Answering by Search and Reading](https://mlsanity.com/paper/1707.03904/) (2017)
- [Learning Kernel-Smoothed Machine Translation with Retrieved Examples](https://mlsanity.com/paper/2109.09991/) (2021)
- [Efficient Cluster-Based k-Nearest-Neighbor Machine Translation](https://mlsanity.com/paper/2204.06175/) (2022)
- [Towards Robust k-Nearest-Neighbor Machine Translation](https://mlsanity.com/paper/2210.08808/) (2022)
- [Efficient Machine Translation Domain Adaptation](https://mlsanity.com/paper/2204.12608/) (2022)
- [What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation](https://mlsanity.com/paper/2211.04052/) (2022)

## Built on by (most cited first, 3 of 3)

- [NNOSE: Nearest Neighbor Occupational Skill Extraction](https://mlsanity.com/paper/2401.17092/) (2024)
- [Multilingual k-Nearest-Neighbor Machine Translation](https://mlsanity.com/paper/2310.14644/) (2023)
- [Quality Estimation with k-nearest Neighbors and Automatic Evaluation for Model-specific Quality Estimation](https://mlsanity.com/paper/2404.18031/) (2024)

## Abstract (arXiv)

Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT). In this paper, we introduce a unified framework kNN-BOX, which enables quick development and interactive analysis for this novel paradigm. kNN-BOX decomposes the datastore-augmentation approach into three modules: datastore, retriever and combiner, thus putting diverse kNN generation methods into a unified way. Currently, kNN-BOX has provided implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. It is easy for users to reproduce these existing works or customize their own models. Besides, users can interact with their kNN generation systems with kNN-BOX to better understand the underlying inference process in a visualized way. In the experiment section, we apply kNN-BOX for machine translation and three other seq2seq generation tasks, namely, text simplification, paraphrase generation and question generation. Experiment results show that augmenting the base neural model with kNN-BOX leads to a large performance improvement in all these tasks. The code and document of kNN-BOX is available at https://github.com/NJUNLP/knn-box.