The paper introduces TITAN, a mechanistic framework that uses causal intervention, sparse feature decomposition, and targeted preference optimization to discover and fine‑tune specific translation circuits in multilingual decoder‑only large language models. By restricting updates to less than 5% of the parameters, TITAN achieves significant BLEU score improvements—33.75% for Hindi‑to‑Urdu and 45.86% for Hindi‑to‑Sindhi—while human evaluations confirm gains in adequacy and fluency. The study also reveals that multilingual translation operates through sparse, structured circuits over a shared semantic subspace rather than relying on an English‑centric pivot representation.
arXiv:2608. 07629v1 Announce Type: cross Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon.
arXiv:2604. 03532v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult.
arXiv:2608.12018v2 Announce Type: replace Abstract: Neural Machine Translation (NMT) and Large Language Models (LLMs) excel at cross-lingual tasks but often fail to capture intra-lingual morphologica...
EnSiTa is a trilingual multi‑domain parallel dataset and benchmark for English, Sinhala, and Tamil. It contains human post‑edited training data across seven domains and professionally translated test sets for those domains plus an additional one, all produced through a multi‑year, rigorously quality‑controlled process. The authors use EnSiTa to conduct a comprehensive study of domain‑specific machine translation across six language directions, comparing from‑scratch Transformers, pre‑trained models, and decoder‑only LLMs under various training‑data sizes, model scales, and domain settings.
The paper investigates weight‑space merging of independently fine‑tuned multilingual machine translation models. Experiments show that merging is more successful when models share a target language, yet it still cannot match the peak performance of language‑specific checkpoints. When target languages differ, performance drops sharply, and analysis reveals that overlapping neuron activation and incompatible upper‑layer geometries cause these failures.
arXiv:2610.18818v2 Announce Type: cross Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource langu...
arXiv:2609.40181v1 Announce Type: new Abstract: We introduce Index-Translate, a multilingual translation model family that combines a shared multilingual foundation with specialized training for gene...
Large language models show strong cross‑lingual abilities, but previous studies have examined them only at isolated training points. This work pretrains a 1.7B multilingual model on nine languages, recording checkpoints at fine granularity to track translation development. Using word‑level translation and a new dataset, the authors find that early learning is dominated by token‑level copying and surface similarities, while later stages involve more general translation mechanisms and refined copying.
arXiv:2608. 04586v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT).
arXiv:2608. 04586v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT).
arXiv:2509.17930v3 Announce Type: replace-cross Abstract: Multilingual translation suffers from computational redundancy, especially when translating into multiple languages simultaneously. In additi...
The paper introduces AssameseLM-E2B, a 2‑billion‑parameter language model tailored for the low‑resource Assamese language, and its instruction‑tuned variant AssameseLM-E2B‑it. Built on the Gemma‑4 architecture, it uses an expanded byte‑level BPE vocabulary that cuts tokenization overhead by nearly 500 % compared with multilingual baselines, and employs a parameter‑efficient Continual Pre‑Training pipeline with Hybrid LoRA followed by supervised fine‑tuning on a balanced, multi‑domain dataset. Evaluation shows strong performance with a Bits‑per‑Byte score of 0.7488 on FLORES‑200, a 96.7 % RAG score, and 100 % refusal accuracy on adversarial prompts, highlighting the effectiveness of parameter‑efficient adaptation for locally deployable models under constrained resources.