mlsanityData to 14 Oct 2026

Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild

arXiv 2605.22064 ↗First posted 21 May 2026cs.CLcited by 10

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Abstract (arXiv)

Hy-MT2 is a family of fast-thinking multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of which support translation among 33 languages and effectively follow translation instructions in multiple languages. Multi-dimensional evaluations show that Hy-MT2 delivers outstanding performance across general, real-world business, domain-specific, and instruction-following translation tasks. The 7B and 30B models outperform open-source models such as DeepSeek-V4-Pro and Kimi K2.6 in fast-thinking mode, while the lightweight 1.8B model also surpasses mainstream commercial APIs from providers such as Microsoft and Doubao overall. Moreover, when paired with AngelSlim's 1.25-bit extreme quantization for on-device deployment, the lightweight 1.8B model requires only 440 MB of storage and achieves a 1.5x inference speedup.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 21 May 2026.

Builds on

9 of 15 references.

References · 9

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  2. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  3. 2023Instruction-Following Evaluation for Large Language Modelscited by 1,228
  4. 2024Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Followingcited by 102
  5. 2024BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillationcited by 84
  6. 2025ParetoQ: Improving Scaling Laws in Extremely Low-bit LLM Quantizationcited by 46
  7. 2025MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluationcited by 7
  8. 2026AngelSlim: A more accessible, comprehensive, and efficient toolkit for large model compressioncited by 6
  9. 2026Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsificationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 10.

10 papers.

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  1. 2025HY-MT1.5 Technical Report
  2. 2026CAT-Translate: Building Compact Open-Source Models for Japanese-English Translation
  3. 2024MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation
  4. 2024Choose the Final Translation from NMT and LLM hypotheses Using MBR Decoding: HW-TSC's Submission to the WMT24 General MT Shared Task
  5. 2025Hunyuan-MT Technical Report
  6. 2024GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators
  7. 2024Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact
  8. 2024X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale