# Analysis of Levenshtein Transformer's Decoder and Its Variants

arXiv: https://arxiv.org/abs/2402.12249 · HTML: https://mlsanity.com/paper/2402.12249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Speculative decoding, one of 12 directions in Efficient LLM inference and serving.
- Builds on 4 of its 5 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Placed in

- Efficient LLM inference and serving / [Speculative decoding](https://mlsanity.com/inference/speculative-decoding/)

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/1606.07947/) (2016)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Understanding Knowledge Distillation in Non-autoregressive Machine Translation](https://mlsanity.com/paper/1911.02727/) (2019)

## Abstract (arXiv)

Levenshtein transformer (LevT) is a non-autoregressive machine translation model with high decoding efficiency and comparable translation quality in terms of bleu score, due to its parallel decoding and iterative refinement procedure. Are there any deficiencies of its translations and what improvements could be made? In this report, we focus on LevT's decoder and analyse the decoding results length, subword generation, and deletion module's capability. We hope to identify weaknesses of the decoder for future improvements. We also compare translations of the original LevT, knowledge-distilled LevT, LevT with translation memory, and the KD-LevT with translation memory to see how KD and translation memory can help.