Speculative decoding (SD) improves LLM inference latency by speculatively generating multiple tokens with a small draft model and verifying them with a larger target model. However, when speculation accuracy is low, the overhead from rejected tokens can negate its benefits, especially at large batch sizes.We propose Speculative Verification (SV), an efficient augmentation to SD that predicts speculation accuracy and dynamically adapts the verification length to maximize throughput. SV introduces a small companion model, similar in size to draft model, to reduce uncertainty in speculation accuracy. By exploiting the information gain from observing the companion distribution, SV reduces wasted verification on rejected tokens and improves decoding efficiency.We evaluate SV across publicly available LLMs on seven NLP tasks using over a hundred combinations of draft, companion, and target models, including 13B–72B target models spanning base, instruction-tuned, and task-specific fine-tuned variants. Compared to target-only decoding, standard SD, and state-of-the-art SD variants, SV consistently delivers higher throughput across batch sizes. SV improves SD performance by up to 1.9×, with an average 1.4× speedup at large batch sizes, showing robust and scalable gains for practical LLM inference.

This paper describes the Samsung Research’s Translation system (SRT) submitted to the WMT22 biomedical translation task in two language directions: English to Spanish and Spanish to English. To improve the overall quality, we adopt the deep transformer architecture and employ the back-translation strategy for monolingual corpus. One of the issues in the domain translation is to translate domain-specific terminologies well. To address this issue, we apply the soft-constrained terminology translation based on biomedical terminology dictionaries. In this paper, we provide the performance of our system with WMT20 and WMT21 biomedical testsets. Compared to the best model in WMT20 and WMT21, our system shows equal or better performance. According to the official evaluation results in terms of BLEU scores, our systems get the highest scores in both directions.