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Mathias Vast

SIGIR 2026×2可解释性多模态效率与系统检索与知识
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0 一作 · 2 篇 · SIGIR 2026

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2026 顶会论文

2
作者位次 #2/5SIGIR 2026Oral· Information Retrieval (Full)

From Tokens to Concepts: Leveraging SAE for SPLADE

Yuxuan Zong · Mathias Vast · Basile Van Cooten · Laure Soulier · Benjamin Piwowarski

摘要

Learned Sparse IR models, such as SPLADE, offer an excellent efficiency-effectiveness tradeoff. However, they rely on the underlying backbone vocabulary, which might hinder performance (polysemicity and synonymy) and pose a challenge for multi-lingual and multi-modal usages. To solve this limitat...

可解释性多模态效率与系统OpenReview
作者位次 #2/7SIGIR 2026Poster· Information Retrieval (Demo)

IR Lens: A Tool for Interpreting Cross-Encoder Models

Mihai Branga-Peicu · Mathias Vast · Basile Van Cooten · Laure Soulier · Jules Françoise · Benjamin Piwowarski · Baptiste Caramiaux

摘要

Transformer-based ranking models, such as MonoBERT, are central to Information Retrieval; yet their inner workings remain largely opaque. This hinders not only our understanding of the systems implementing them, but also our ability to improve them. To alleviate this limitation, we introduce IR L...

可解释性检索与知识OpenReview

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