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English(EN) The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge

Eloquence 团队详解其在 Interspeech 2026 挑战赛中的多语言问答方法

Eloquence 团队详细介绍了他们为 Interspeech 2026 MLC-SLM 挑战赛提交的方案,该方案专注于 21 种语言的多语言选择题问答。他们探索了三种方法:使用 LoRA 和数据增强对 Voxtral-Mini-3B 进行微调,取得了 0.72 的宏观准确率;将多模态上下文学习应用于 Voxtral-24B,获得了他们最佳的 0.81 成绩;以及一个得分 0.68 的检索系统。所有三种方法均超越了基线性能。 AI

影响 这项研究为多语言问答和多模态学习技术的发展做出了贡献。

排序理由 该集群描述了一篇研究论文,其中详细介绍了提交给学术挑战赛的方案,包括模型性能和方法。

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Eloquence 团队详解其在 Interspeech 2026 挑战赛中的多语言问答方法

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该集群描述了一篇研究论文,其中详细介绍了提交给学术挑战赛的方案,包括模型性能和方法。
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报道来源 [2]

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

    Interspeech 2026 MLC-SLM挑战赛任务2的Eloquence提交

    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) ·

    Interspeech 2026 MLC-SLM挑战赛任务2的Eloquence提交

    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…