We present the findings of the WMT26 Multilingual Instruction Shared Task (MIST), evaluating small (=10B parameter) open-weight language models across 24 languages on three sub-tasks: context-based QA, cross-lingual summarization, and open-ended generation. Submissions from eight teams explore fine-tuning, knowledge distillation, and prompt engineering. Our findings demonstrate that small models are not yet polyglots: while leading systems perform well on high-resource languages, performance drops sharply on low-resource varieties, and comprehension degrades significantly when transferring across non-English language pairs. In open-ended generation, human evaluation and automatic rule-based verification exhibit strong overall rank correlation (0.79) but diverge locally at the top: human evaluators favor distilled pipelines with superior fluency and cross-lingual equity, whereas automatic verifiers reward concise prompt-engineered systems. Furthermore, multi-stage pipelines suffer from prompt-language inertia on cross-lingual instructions, and summarization exhibits severe English leakage unless penalized by language gating. Finally, while baseline fluency is largely achieved by modern small models, multi-constraint instruction following and unanswerable question refusal remain the primary performance bottlenecks. We release all test sets, system outputs, human judgments, and evaluation code under a permissive license.

This paper reports on the outcomes of the shared tasks organized as part of the 23rd International Workshop on Spoken Language Translation (IWSLT). The workshop covered ten major challenges in spoken language translation, including speech-to-text translation for both high-resource and low-resource language pairs, customized speech translation, speech generation, instruction-following speech processing, and the evaluation of speech translation systems. The shared tasks received strong participation, with more than 30 teams submitting runs. This year’s edition broadened the range of tasks, placing particular emphasis on speech generation and evaluation metrics.

Developing a machine translation (MT) system requires a considerable amount of high-quality parallel data, which is often limited for low-resource languages. This paper explores the use of synthetic data for training an LLM-based MT system in the English-to-Basque direction. Using Basque monolingual corpora as a starting point, we apply back-translation to generate parallel corpora, taking advantage of the fact that current LLMs do not translate well from English to Basque, but they yield an acceptable performance in the reverse direction. We conduct experiments in a multi-stage approach, from a simple Supervised Fine-tuning (SFT) step, to preference learning with the Direct Preference Optimization (DPO) technique. We then evaluate the approach with both automatic metrics and manual assessment. Experimental results suggest that for this task, SFT brings a clear improvement in translation quality, while DPO only yields marginal enhancement.

This paper describes the CUNI-NL team’s submission to the IWSLT 2025 Offline Speech Translation and Instruction Following tasks, focusing on transcribing the English audio, and translating the English audio to German text. Our systems follow the end-to-end approach, where each system consists of a pretrained, frozen speech encoder, along with a medium-sized large language model fine-tuned with LoRA on three tasks: 1) transcribing the English audio; 2) directly translating the English audio to German text; and 3) a combination of the above two tasks, i.e. simultaneously transcribing the English audio and translating the English audio to German text.

This paper presents the contributions of Charles University teams to the WMT24 General Translation task (English to Czech, German and Russian, and Czech to Ukrainian), and the WMT24 Translation into Low-Resource Languages of Spain task.Our most elaborate submission, CUNI-MH for en2cs, is the result of fine-tuning Mistral 7B v0.1 for translation using a three-stage process: Supervised fine-tuning using QLoRA, Contrastive Preference Optimization, and merging of model checkpoints. We also describe the CUNI-GA, CUNI-Transformer and CUNI-DocTransformer submissions, which are based on our systems from the previous year.Our en2ru system CUNI-DS uses a similar first stage as CUNI-MH (QLoRA for en2cs) and follows with transferring to en2ru.For en2de (CUNI-NL), we experimented with a LLM-based speech translation system, to translate without the speech input.For the Translation into Low-Resource Languages of Spain task, we performed QLoRA fine-tuning of a large LLM on a small amount of synthetic (backtranslated) data.