Many important tasks in Natural Language Processing (NLP), including information retrieval, classification, or regression, are built upon general-purpose vector representations. These representations are traditionally obtained from bidirectional encoder models, which aggregate information from the left and right contexts of each token (Devlin et al., 2019; Conneau et al., 2020; He et al., 2023). In contrast, recent advances in generative modeling have shifted the research community's attention towards unidirectional architectures (Bai et al., 2023; Llama Team, 2024; OLMo et al., 2025). Notably, these efforts have identified several key performance drivers that span architectural advances, data improvements, and increased scale. Yet, despite no apparent barrier to transferring these insights to bidirectional architectures, little effort has been devoted towards this objective, forcing practitioners to depend on outdated models. In this paper, we introduce a refreshed recipe for training general-purpose multilingual encoders, resulting in the EuroBERT family. Drawing inspiration from recent progress in decoder models, our models feature an updated architecture ( 2.1), and are trained on a 5T-token multilingual dataset, covering widely spoken European and global languages,

The quality of open-weight LLMs has seen significant improvement, yet they remain predominantly focused on English. In this paper, we introduce the EuroLLM project, aimed at developing a suite of open-weight multilingual LLMs capable of understanding and generating text in all official European Union languages, as well as several additional relevant languages. We outline the progress made to date, detailing our data collection and filtering process, the development of scaling laws, the creation of our multilingual tokenizer, and the data mix and modeling configurations. Additionally, we release our initial models: EuroLLM-1.7B and EuroLLM-1.7B-Instruct and report their performance on multilingual general benchmarks and machine translation.

Many important tasks within multilingual NLP, such as quality estimation, automatic postedition, or grammatical error correction, involve analyzing, generating or operating with text in multiple languages, and are relevant to various translation workflows -- we call these translation-related tasks. Recently, general-purpose large language models (LLMs) challenged the paradigm of per-task dedicated systems, achieving state-of-the-art performance on several recent WMT shared tasks (Kocmi et al., 2023; Freitag et al., 2023; Neves et al., 2023). Unfortunately, strong capabilities for multiple translation-related tasks have so far been exhibited by closed LLMs only (Hendy et al., 2023; Kocmi & Federmann, 2023; Fernandes et al., 2023; Raunak et al., 2023). Perhaps because most open LLMs are English-centric, approaches leveraging these models still lag behind, having thus far achieved competitive results only when specializing on a single task (Xu et al., 2024a; 2023; Iyer et al., 2023). In this paper, we bridge this gap with a detailed recipe to develop an LLM for multiple translation-related tasks. Our approach, illustrated in Figure 1 and inspired by Xu et al.

We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81 % of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.

Large language models (LLMs) are a promising avenue for machine translation (MT). However, current LLM-based MT systems are brittle: their effectiveness highly depends on the choice of few-shot examples and they often require extra post-processing due to overgeneration. Alternatives such as finetuning on translation instructions are computationally expensive and may weaken in-context learning capabilities, due to overspecialization. In this paper, we provide a closer look at this problem. We start by showing that adapter-based finetuning with LoRA matches the performance of traditional finetuning while reducing the number of training parameters by a factor of 50. This method also outperforms few-shot prompting and eliminates the need for post-processing or in-context examples. However, we show that finetuning generally degrades few-shot performance, hindering adaptation capabilities. Finally, to obtain the best of both worlds, we propose a simple approach that incorporates few-shot examples during finetuning. Experiments on 10 language pairs show that our proposed approach recovers the original few-shot capabilities while keeping the added benefits of finetuning.