# LLMs Are Zero-Shot Context-Aware Simultaneous Translators

arXiv: https://arxiv.org/abs/2406.13476 · HTML: https://mlsanity.com/paper/2406.13476/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2024.

## Built on by (most cited first, 10 of 10)

- [Efficient and Adaptive Simultaneous Speech Translation with Fully Unidirectional Architecture](https://mlsanity.com/paper/2504.11809/) (2025)
- [LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline](https://mlsanity.com/paper/2504.09570/) (2025)
- [SimulS2S-LLM: Unlocking Simultaneous Inference of Speech LLMs for Speech-to-Speech Translation](https://mlsanity.com/paper/2504.15509/) (2025)
- [From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models](https://mlsanity.com/paper/2603.04592/) (2026)
- [JobResQA: A Benchmark for LLM Machine Reading Comprehension on Multilingual R'esum'es and JDs](https://mlsanity.com/paper/2601.23183/) (2026)
- [Team ACK at SemEval-2025 Task 2: Beyond Word-for-Word Machine Translation for English-Korean Pairs](https://mlsanity.com/paper/2504.20451/) (2025)
- [OmniFusion: Simultaneous Multilingual Multimodal Translations via Modular Fusion](https://mlsanity.com/paper/2512.00234/) (2025)
- [From Oracle to Noisy Context: Mitigating Contextual Exposure Bias in Speech-LLMs](https://mlsanity.com/paper/2603.24034/) (2026)
- [MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task](https://mlsanity.com/paper/2606.17255/) (2026)
- [All In Good Time: Causality-Aware Framework for LLM-Based Simultaneous Speech-to-Speech Translation](https://mlsanity.com/paper/2609.30416/) (2026)

## Abstract (arXiv)

The advent of transformers has fueled progress in machine translation. More recently large language models (LLMs) have come to the spotlight thanks to their generality and strong performance in a wide range of language tasks, including translation. Here we show that open-source LLMs perform on par with or better than some state-of-the-art baselines in simultaneous machine translation (SiMT) tasks, zero-shot. We also demonstrate that injection of minimal background information, which is easy with an LLM, brings further performance gains, especially on challenging technical subject-matter. This highlights LLMs' potential for building next generation of massively multilingual, context-aware and terminologically accurate SiMT systems that require no resource-intensive training or fine-tuning.