Machine Translation

Reproducibility is essential for scientific research, yet prior work shows that LLM outputs vary with hardware and batching. We identify an overlooked factor: the hidden injection of the current date into system prompts,…

Machine Translation Code Generation

5-Dialects-BN: Unmasking the Impact of Transliteration on Bangla Dialectal LLMs

2026-09-09 · Md Mahir Jawad, Galib Mahmud Jim, Rafid Ahmed, Mir Sazzat Hossain 외 arxiv

Large Language Models (LLMs) have achieved remarkable progress across natural language processing (NLP) tasks, yet their capabilities degrade sharply for low-resource languages and dialectally diverse settings. Bangla, t…

parameter-efficient fine-tuning Machine Translation

EuroAlpaca: Task-Preserving Localisation of Instruction Data for European Languages

2026-09-04 · Aleix Sant, Jordi Luque, Carlos Escolano arxiv

Machine translation (MT) offers a scalable way to extend English instruction-tuning data to multiple languages, but it can distort task-critical constraints and required outputs, creating corrupted training examples and …

Instruction Following Machine Translation

Discourse Dependency: A Continuous Criterion for Translation Difficulty

2026-09-04 · Ahrii Kim, Chanjun Park, Seong-heum Kim arxiv

Recent calls for harder machine translation benchmarks have not clarified what difficulty should mean. We argue that one meaningful and currently unmeasured axis is referential reach, the distance a segment must look bac…

Machine Translation

Last Translation Benchmark

2026-09-03 · Vilém Zouhar, Niyati Bafna, Mukund Choudhary, Maike Züfle 외 hf

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, standard benchmarks for machine translation …

Machine Translation

Ladders in Chaos: When, How, (and Perhaps Why) Does Test-Time Scaling Improve LLM Machine Translation

2026-08-28 · Di Wu, Sergey Troshin, Christof Monz, Antske Fokkens 외 arxiv

Two forms of test-time scaling for Large Language Models (LLMs) have emerged as effective and widely adopted paradigms: sequential, in which later answer attempts depend on earlier ones, and parallel, such as i.i.d. samp…

Machine Translation

전체 11,223편 보기 →