# Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models

arXiv: https://arxiv.org/abs/2402.10552 · HTML: https://mlsanity.com/paper/2402.10552/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Monotonic Infinite Lookback Attention for Simultaneous Machine Translation](https://mlsanity.com/paper/1906.05218/) (2019)
- [Learning to Translate in Real-time with Neural Machine Translation](https://mlsanity.com/paper/1610.00388/) (2016)
- [SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation](https://mlsanity.com/paper/2011.02048/) (2020)
- [Adapting Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2401.06468/) (2024)
- [TIM: Teaching Large Language Models to Translate with Comparison](https://mlsanity.com/paper/2307.04408/) (2023)
- [Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection](https://mlsanity.com/paper/2005.11185/) (2020)
- [Information-Transport-based Policy for Simultaneous Translation](https://mlsanity.com/paper/2210.12357/) (2022)
- [TransLLaMa: LLM-based Simultaneous Translation System](https://mlsanity.com/paper/2402.04636/) (2024)
- [CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022](https://mlsanity.com/paper/2204.06028/) (2022)
- [Augmenting Large Language Model Translators via Translation Memories](https://mlsanity.com/paper/2305.17367/) (2023)
- [Super-Human Performance in Online Low-latency Recognition of Conversational Speech](https://mlsanity.com/paper/2010.03449/) (2020)
- [Wait-info Policy: Balancing Source and Target at Information Level for Simultaneous Machine Translation](https://mlsanity.com/paper/2210.11220/) (2022)
- [Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning](https://mlsanity.com/paper/2002.04306/) (2020)

## Built on by (most cited first, 4 of 4)

- [LiveMind: Low-latency Large Language Models with Simultaneous Inference](https://mlsanity.com/paper/2406.14319/) (2024)
- [SimulPL: Aligning Human Preferences in Simultaneous Machine Translation](https://mlsanity.com/paper/2502.00634/) (2025)
- [SimulSense: Sense-Driven Interpreting for Efficient Simultaneous Speech Translation](https://mlsanity.com/paper/2509.21932/) (2025)
- [Dual-Stream Simultaneous Translation via 2D Grid Attention](https://mlsanity.com/paper/2609.34902/) (2026)

## Abstract (arXiv)

Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency. Recent studies have shown that LLMs can achieve good performance in SimulMT tasks. However, this often comes at the expense of high inference cost and latency. In this paper, we propose a conversational SimulMT framework to enhance the inference efficiency of LLM-based SimulMT through multi-turn-dialogue-based decoding. Our experiments with Llama2-7b-chat on two SimulMT benchmarks demonstrate the superiority of LLM in translation quality while achieving comparable computational latency to specialized SimulMT models.