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Trillion 7B Technical Report

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arxiv 2504.15431 v1 pith:TOEZ7OUR submitted 2025-04-21 cs.CL cs.AIcs.LG

Trillion 7B Technical Report

Sungjun Han , Juyoung Suk , Suyeong An , Hyungguk Kim , Kyuseok Kim , Wonsuk Yang , Seungtaek Choi , Jamin Shin (Trillion Labs) This is my paper
classification cs.CL cs.AIcs.LG
keywords multilingualtrillion-7bcross-lingualdatalanguagesperformancetrainingachieves
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduce Trillion-7B, the most token-efficient Korean-centric multilingual LLM available. Our novel Cross-lingual Document Attention (XLDA) mechanism enables highly efficient and effective knowledge transfer from English to target languages like Korean and Japanese. Combined with optimized data mixtures, language-specific filtering, and tailored tokenizer construction, Trillion-7B achieves competitive performance while dedicating only 10\% of its 2T training tokens to multilingual data and requiring just 59.4K H100 GPU hours (\$148K) for full training. Comprehensive evaluations across 27 benchmarks in four languages demonstrate Trillion-7B's robust multilingual performance and exceptional cross-lingual consistency.

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Cited by 1 Pith paper

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  1. Optimizing Korean-Centric LLMs via Token Pruning

    cs.CL 2026-04 unverdicted novelty 4.0

    Token pruning of non-Korean vocabulary in LLMs improves generation stability and often boosts machine translation on Korean tasks while cutting vocabulary size substantially.