Papers Machine Translation
“Machine Translation” 태그가 달린 논문 11,223편 · 필터 해제
Dating the Model: Hidden Dates in System Prompts Affect LLM Evaluation
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 TranslationCode Generation5-Dialects-BN: Unmasking the Impact of Transliteration on Bangla Dialectal LLMs
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-tuningMachine TranslationEuroAlpaca: Task-Preserving Localisation of Instruction Data for European Languages
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 FollowingMachine TranslationDiscourse Dependency: A Continuous Criterion for Translation Difficulty
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 TranslationLast Translation Benchmark
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 TranslationLadders in Chaos: When, How, (and Perhaps Why) Does Test-Time Scaling Improve LLM Machine Translation
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 TranslationSTAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translation
Large Language Models (LLMs) have enabled a shift from sentence-level to document-to-document (Doc2Doc) machine translation, promising improved global coherence. However, document-to-document generation in a single pass …
Machine TranslationReasoning about In-Context Samples for Machine-Translation
Large Language Models (LLMs) can be trained to perform chain-of-thoughts reasoning in order to improve the reliability of their responses. In this work, we investigate how explicit reasoning can be leveraged for LLM-Base…
Machine TranslationStatistical Machine Translation Systems of English-Pnar Language Pair : Some Insights of the Emperical Study
Pnar, an Austroasiatic language spoken by approximately 0.4 million people in the Jaintia Hills of Meghalaya, lacks the digital corpora and natural language processing (NLP) resources. This paper presents the first machi…
Machine TranslationLength-Adaptive Decoding for Masked Diffusion Machine Translation
Machine translation tests masked diffusion language models (dLLMs) because every source token must be rendered faithfully, while fixed canvas decoding must choose target length before denoising. Existing masked diffusion…
Machine TranslationSource-Free MT Evaluation Is Not MT Evaluation
Reference-based metrics remain the standard choice in machine translation evaluation, partly because quality estimation methods often correlate less well with human judgments. As a result, source-free, reference-based ev…
Machine TranslationAssessing Quality of Experience in Natural Language Generation of German Text
The rapid advancement of Natural Language Generation (NLG) has made the reliable evaluation of generated text increasingly critical, as these systems, such as large language models (LLMs), are now widely deployed in real…
Machine TranslationText SummarizationTranslatePsy-AfriSLM: High-Quality Data Scaling For Low-Resource Machine Translation
The rapid progress in Artificial Intelligence has largely bypassed African languages, creating a digital divide that limits AI adoption on the continent. Recent open-source LLMs systematically underperform on African lan…
Machine TranslationDynamic Multi-Byte Prediction With Hierarchical Language Models
Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization. However, generating one byte at a time remains a bottleneck for inf…
Instruction FollowingMachine TranslationQuestion AnsweringReference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation
We study reference-free post-training for multilingual machine translation with open large language models. Starting from the supervised-finetuned MiLMMT-46-v0.1 models, we apply Group Relative Policy Optimization (GRPO)…
Language IdentificationReinforcement LearningMachine TranslationMitigating Gender Bias in English to Romanian Machine Translation
Machine translation (MT) systems often fail to correctly translate gender, especially when converting from a gender-neutral language like English to a gendered target language such as Romanian. This bias results in trans…
Machine TranslationEfficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Study
The adoption of large pre-trained multilingual models for neural machine translation (MNMT) faces a major challenge: excessive memory and computational consumption due to overly large vocabularies and embedding layers. A…
Knowledge DistillationMachine TranslationPredicting Multilingual Classification and Translation Performance of LLMs with Cross-Lingual Alignment -- Is English Enough?
Multilingual large language models (LLMs) have been shown to perform better on non-English classification tasks when the representations of the given language are more aligned to English within the model. Several cross-l…
Machine TranslationPAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translation
Multi-domain machine translation (MDMT) requires more than fluent generation: it demands domain-sensitive translation decisions such as domain disambiguation, terminology control, and stylistic adaptation. Large reasonin…
Reinforcement LearningMachine TranslationLarge Language Models for Low-Resource Languages: A Conceptual Framework for an Electronic Explanatory Dictionary of the Tajik Language
This paper presents a conceptual framework for developing an electronic explanatory dictionary of the Tajik language using large language models (LLMs). The relevance of the work stems from the absence of a comprehensive…
parameter-efficient fine-tuningMachine TranslationSentiment Analysis