A novel fragment-based reasoning framework is introduced in which the model first extracts parallel source-target fragments from retrieved similar exemplars, and uses these fragments as intermediate reasoning traces to produce the final translation.
Abstract
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-Based Machine Translation (MT) with in-context samples. We introduce a novel fragment-based reasoning framework in which the model first extracts parallel source-target fragments from retrieved similar exemplars, and uses these fragments as intermediate reasoning traces to produce the final translation. To train our model, we distill silver fragments and drafts from a large teacher model. Our experiments with the Qwen3 model family, over 6 languages, including up to 5 domains per language, demonstrate that fragment-based MT significantly outperforms alternative methods like standard k-shot or basic drafting.
The results show that the quality of small ``base'' models can be greatly enhanced, and that reinforcement learning with verifiable rewards can be applied to NMT in the legal domain and surpasses the translation quality of supervised fine-tuning.
Aixiu An, Michael Jungo, Eloi Eynard et al.· arXiv.org· 0 citations
This work introduces Structured Thoughts, a framework that organizes reasoning into alternating blocks and blocks that captures exploratory scratch work, while the distilled conclusion of that step contains the distilled conclusion of that step.
Reasoning Language Models (RLMs) achieve their strongest performance when they reason in English, the language for which reasoning-oriented training data is most abundant. However, reasoning trace is a clue for model interpretability and safety, and useful in practice for both the model users and for model developers....
Large language models have high computation and inference costs. Recently, Small Language Models (SLMs) have become more important because they require fewer resources and offer high efficiency. Different training approaches can be used for SLMs to achieve high performance, even on resource-constrained hardware.In this...
Ali Efe Çoban, Oguz Dikenelli· Signal Processing and Commun...· 0 citations
A Tree-of-Thoughts (ToT) reasoning framework for T2I-ICL is proposed that introduces a multi-stage reasoning and selection layer that generates, evaluates, and selects among multiple candidate hypotheses before constructing the final prompt for image synthesis.
S. Alekseeva, J. Kalafatovich, Seong-Whan Lee· arXiv.org· 0 citations
This work proposes Efficient Chain-of-Modality Reasoning (ECoM Reasoning), the first framework to introduce compressed reasoning into SLMs, and proposes Progressive Compression, a curriculum-based strategy that gradually trains the model from full-form reasoning to compressed reasoning.
Pengchao Feng, Chao-Hong Tan, Qian Chen et al.· 0 citations
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