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Eloquence team details multilingual QA approach for Interspeech 2026 challenge

By PulseAugur Editorial · [2 sources] ·

The Eloquence team detailed their submission for the Interspeech 2026 MLC-SLM challenge, focusing on multilingual multiple-choice question answering across 21 languages. They explored three methods: fine-tuning Voxtral-Mini-3B with LoRA and data augmentation, achieving 0.72 macro-accuracy; applying multimodal in-context learning to Voxtral-24B, yielding their best result of 0.81; and a retrieval system that scored 0.68. All three approaches surpassed the baseline performance. AI

IMPACT This research contributes to advancements in multilingual question answering and multimodal learning techniques.

RANK_REASON The cluster describes a research paper detailing a submission to an academic challenge, including model performance and methodologies.

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Eloquence team details multilingual QA approach for Interspeech 2026 challenge

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Jordi Luque, Lorenzo Concina, Marco Matassoni, Alessio Brutti, Filippo Vella ·

    The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge

    arXiv:2609.11724v1 Announce Type: new Abstract: This paper details the Eloquence team's approach to Task 2 of the 2nd MLC-SLM challenge at Interspeech 2026, which involves multilingual Multiple-Choice Question Answering (MCQA) across 21 languages. Three approaches are explored. F…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge

    This paper details the Eloquence team's approach to Task 2 of the 2nd MLC-SLM challenge at Interspeech 2026, which involves multilingual Multiple-Choice Question Answering (MCQA) across 21 languages. Three approaches are explored. First, we fine-tune Voxtral-Mini-3B via LoRA with…

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