MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond
- URL: http://arxiv.org/abs/2607.22100v1
- Date: Fri, 24 Jul 2026 08:52:44 GMT
- Title: MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond
- Abstract summary: We introduce MEUSLI, the first open-science multilingual projector family.<n>It links a Whisper encoder with open-source multilingual LLMs, enabling fully open-source end-to-end ASR in 28 European languages.<n>Using proper continual leaning techniques, MEUSLI can be easily extended to other languages not seen in training.
- Score: 7.8241547017865045
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Lightweight projectors are an established way to connect pre-trained speech encoders with large language models (LLMs), mapping acoustic features into token-level embeddings for tasks like ASR and spoken question answering. Existing systems, however, typically only support a few languages and are often limited to English. We introduce MEUSLI, the first open-science multilingual projector family that links a Whisper encoder with open-source multilingual LLMs, enabling fully open-source end-to-end ASR in 28 European languages. MEUSLI extends prior monolingual pipelines, delivering strong results across high- and low-resource languages. Using proper continual leaning techniques, MEUSLI can be easily extended to other languages not seen in training. We further demonstrate that the MEUSLI projector can be leveraged beyond ASR, enabling multilingual speech translation and topic identification with only a few hours of task specific supervision per language. Overall, MEUSLI provides a solid foundation for multilingual speech understanding tasks, supporting scalable and inclu- sive open-source SpeechLLM
Related papers
- Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages [15.84874997729878]
This tutorial offers an overview of this emerging research area for multilingual multimodality across text, speech, and vision.<n>We cover low-cost data creation/curation; adapter stacks for tri-modal alignment; culture-aware evaluation beyond English.<n>The content will be delivered as an interactive half-day tutorial, designed for researchers and practitioners working on multilingual, multimodal AI in low-resource language settings.
arXiv Detail & Related papers (2026-05-16T20:56:15Z) - Language-Aware Distillation for Multilingual Instruction-Following Speech LLMs with ASR-Only Supervision [36.3996368188181]
Speech Large Language Models (LLMs) that understand and follow instructions in many languages are useful for real-world interaction.<n>Recent distillation-based approaches train performant English-only Speech LLMs using only annotated ASR data by aligning text and speech using only a lightweight projector.<n>We address this by introducing language-aware distillation using a query bank and a gating network that selects or mixes query tokens using a Q-Former projector.
arXiv Detail & Related papers (2026-03-07T04:09:47Z) - Multimodal In-context Learning for ASR of Low-resource Languages [16.078416187950207]
In-context learning (ICL) with large language models (LLMs) addresses this problem.<n>This paper investigates whether speech LLMs can learn unseen languages with multimodal ICL (MICL)<n>Cross-lingual transfer learning improves MICL efficiency on target languages without training on them.
arXiv Detail & Related papers (2026-01-09T10:52:23Z) - Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages [76.14451035425229]
We introduce Omnilingual ASR, a large-scale automatic speech recognition system.<n>It scales self-supervised pre-training to 7B parameters to learn robust speech representations.<n>It expands coverage to over 1,600 languages, including over 500 never before served by ASR.
arXiv Detail & Related papers (2025-11-12T19:48:09Z) - Speech LLMs in Low-Resource Scenarios: Data Volume Requirements and the Impact of Pretraining on High-Resource Languages [9.577509224534323]
Large language models (LLMs) have demonstrated potential in handling spoken inputs for high-resource languages, reaching state-of-the-art performance in various tasks.<n>This work investigates the use of Speech LLMs for low-resource Automatic Speech Recognition using the SLAM-ASR framework.<n>We show that leveraging mono- or multilingual projectors pretrained on high-resource languages reduces the impact of data scarcity.
arXiv Detail & Related papers (2025-08-07T08:33:42Z) - LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for Low-Resource Language Reasoning [28.288949710191158]
Large language models (LLMs) have exhibited impressive multilingual reasoning capabilities, driven by extensive multilingual pre-training corpora and instruction fine-tuning data.<n>A performance gap exists between high- and low-resource language reasoning tasks due to the language imbalance in the pre-training corpus.<n>We propose LinguaLIFT, a two-stage instruction tuning framework for advancing low-resource language reasoning.
arXiv Detail & Related papers (2024-12-17T03:03:17Z) - Lens: Rethinking Multilingual Enhancement for Large Language Models [70.85065197789639]
We propose Lens, a novel approach to enhance multilingual capabilities in large language models (LLMs)<n>Lens operates on two subspaces: the language-agnostic subspace, where it aligns target languages with the central language to inherit strong semantic representations, and the language-specific subspace, where it separates target and central languages to preserve linguistic specificity.<n>Lens significantly improves multilingual performance while maintaining the model's English proficiency, achieving better results with less computational cost compared to existing post-training approaches.
arXiv Detail & Related papers (2024-10-06T08:51:30Z) - Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora.
We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs.
Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
arXiv Detail & Related papers (2024-02-26T09:36:05Z) - UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised
Fine-tuning Dataset [69.33424532827608]
Open-source large language models (LLMs) have gained significant strength across diverse fields.
In this work, we construct an open-source multilingual supervised fine-tuning dataset.
The resulting UltraLink dataset comprises approximately 1 million samples across five languages.
arXiv Detail & Related papers (2024-02-07T05:05:53Z) - Okapi: Instruction-tuned Large Language Models in Multiple Languages
with Reinforcement Learning from Human Feedback [61.83548032416181]
We present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages.
Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
arXiv Detail & Related papers (2023-07-29T18:01:46Z) - Generalizing Multimodal Pre-training into Multilingual via Language
Acquisition [54.69707237195554]
English-based Vision-Language Pre-training has achieved great success in various downstream tasks.
Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training.
We propose a textbfMultitextbfLingual textbfAcquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual.
arXiv Detail & Related papers (2022-05-29T08:53:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.