The paper presents a study on English‑to‑Traditional‑Chinese subtitle translation for a free browser extension that must run on a viewer’s CPU within about 500 ms per subtitle. Two models based on the LMT‑60 architecture, Sotto‑1.7B and a smaller Sotto‑0.6B, were trained with task‑specific vocabulary, continued pretraining, supervised fine‑tuning, weight interpolation, logit distillation, and on‑policy preference optimization. Evaluation via pairwise comparison against Google Translate shows Sotto‑1.7B achieving a 0.576 win rate and outperforming four open translation models, while latency optimizations such as idle‑time prefill and a compact vocabulary reduce end‑to‑end latency by 31% and 28% respectively, keeping the deployed model within the 500 ms target.
arXiv:2607. 09957v1 Announce Type: cross Abstract: This report studies on-device English-to-Traditional-Chinese subtitle translation for Taiwan under short inputs, short outputs, batch-size-one inference, low latency, and privacy constraints.
arXiv:2610.18818v2 Announce Type: cross Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource langu...
The paper introduces a training strategy for cascaded simultaneous speech translation that allows the system to dynamically decide how much of the source prefix to translate. By fine‑tuning a large language model (Qwen3‑8B) on stable prefixes—pairs of source prefixes and the longest shared translation with the full sentence—the authors enable contextual read‑write decisions beyond fixed wait‑k or target‑suffix deletion. Experiments on English‑to‑German, Japanese, and Chinese demonstrate that stable prefixes improve the quality‑latency tradeoff across various test sets.
arXiv:2609.40181v1 Announce Type: new Abstract: We introduce Index-Translate, a multilingual translation model family that combines a shared multilingual foundation with specialized training for gene...
arXiv:2609.15079v1 Announce Type: cross Abstract: Multi-agent LLM architectures, such as LangChain and AutoGen, largely assume English as the lingua franca for internal inter-agent communication, eve...
arXiv:2607.09885v3 Announce Type: replace Abstract: We present Index-1.9B, a series of open small language models developed at Bilibili. The series comprises four models: Index-1.9B-Base, a foundatio...
arXiv:2609.38660v1 Announce Type: cross Abstract: Long-form subtitle translation requires reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consisten...
arXiv:2607.00890v2 Announce Type: replace Abstract: Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synth...
arXiv:2606. 17255v1 Announce Type: cross Abstract: This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2026 Simultaneous Speech Translation track.
arXiv:2606. 24169v1 Announce Type: new Abstract: Adapting a streaming speech recognition model to a new language requires choosing between two plausible warm starts: a multilingual (ML) encoder or an English-only (EN) encoder.
arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.
arXiv:2608. 04586v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT).