aiqbench analysis
The paper trains a small English-to-Traditional-Chinese subtitle translation model that runs on a viewer's CPU within about 500 ms per subtitle. It evaluates by pairwise human-style judging against Google Translate, using two LLM judges, because reference translations are unreliable. Readers working on on-device translation should note the reported gains from idle-time prefill and a task-specific vocabulary, though the win rate of 0.576 and the LLM-judge setup are self-reported and not independently validated.
Context-Aware Subtitle Translation on CPU: Deployment-Driven Training, Evaluation, and Latency Design
arXiv:2607.09957v3 Announce Type: replace Abstract: We study English-to-Traditional-Chinese (Taiwan) subtitle translation for a free browser extension, where the model must run on the viewer's CPU in about 500 ms per subtitle. We train two models built on the LMT-60 base models, Sotto-1.7B and the deployed Sotto-0.6B, with a task-specific vocabulary, continued pretraining, supervised fine-tuning, weight interpolation, logit distillation and on-policy preference optimization. Because reference translations of subtitles are unreliable, we evaluate by pairwise comparison against one fixed baseline, Google Translate, with each pair judged in both orders by a panel of two LLM judges. On 1,820 held-out subtitles, Sotto-1.7B has a win rate of 0.576 against the baseline and wins head-to-head against four open translation models, and is slightly below a 27B model. We split latency into prefill and decoding: idle-time prefill, which prefills the known part of the prompt while waiting for the next subtitle, cuts mean end-to-end latency by 31% on a CPU, and the task-specific vocabulary encodes the same Chinese text in 28% fewer tokens. The deployed model stays within 500 ms for every subtitle in our latency test. The code is available at https://github.com/aiden1020/localsubs .
Abstract:We study English-to-Traditional-Chinese (Taiwan) subtitle translation for a free browser extension, where the model must run on the viewer's CPU in about 500 ms per subtitle. We train two models built on the LMT-60 base models, Sotto-1.7B and the deployed Sotto-0.6B, with a task-specific vocabulary, continued pretraining, supervised fine-tuning, weight interpolation, logit distillation and on-policy preference optimization. Because reference translations of subtitles are unreliable, we evaluate by pairwise comparison against one fixed baseline, Google Translate, with each pair judged in both orders by a panel of two LLM judges. On 1,820 held-out subtitles, Sotto-1.7B has a win rate of 0.576 against the baseline and wins head-to-head against four open translation models, and is slightly below a 27B model. We split latency into prefill and decoding: idle-time prefill, which prefills the known part of the prompt while waiting for the next subtitle, cuts mean end-to-end latency by 31% on a CPU, and the task-specific vocabulary encodes the same Chinese text in 28% fewer tokens. The deployed model stays within 500 ms for every subtitle in our latency test. The code is available at this https URL .
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2607.09957 [cs.CL]
(or arXiv:2607.09957v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.09957 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 282 words