The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge
Updated 5 h ago · first seen 11 Sept 2026
paper_01M294G4SPAFHX5ENQGKVHW5DN
- Published
- 11 Sept 2026
- T1 · 5 h ago
- arXiv
- 2609.11724
- T1 · 5 h ago
- Category
- cs.CL
- T1 · 5 h ago
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. First, we fine-tune Voxtral-Mini-3B via LoRA with cross-lingual data augmentation, ASR transcript augmentation and timestamp-aware audio cropping, achieving 0.72 macro-accuracy on evaluation Phase 2. Second, we apply multimodal in-context learning (ICL) to the frozen Voxtral-24B model to correct a strong label bias, reaching 0.81, our best result. Third, a training-free retrieval system based on a three-layer voice-anchored memory combining acoustic identity, semantic content, and a knowledge graph achieves 0.68. All three systems substantially outperform the official baseline.
Authors 5
Jordi Luque, Lorenzo Concina, Marco Matassoni, Alessio Brutti, Filippo Vella
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
- arXiv id
- 2609.11724
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
- Categories
- cs.CL
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
- Primary category
- cs.CL
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
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T19
Freshest observation
5 h ago
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- Authors
- Jordi Luque, Lorenzo Concina, Marco Matassoni
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Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.CL | feed | T1· Official | 3 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.