Główne pojęcia
提案されたDESIRE-MEモデルは、Mixture-of-Expertsフレームワークを活用して、最先端の密な検索モデルのパフォーマンスを向上させることができます。
Streszczenie
Abstract:
Open-domain question answering requires accurate answers across various query types and topics.
DESIRE-ME leverages the Mixture-of-Experts framework to specialize in multiple domains.
Introduction:
Neural models have reshaped the IR landscape, with dense retrieval techniques showing promise.
Traditional models rely on lexical similarities, while dense retrieval captures semantics better.
Mixture-of-Experts Background:
MoE combines information from specialized experts dedicated to specific domains or sub-tasks.
Gating function determines which expert(s) to use based on input data.
DESIRE-ME Model:
DESIRE-ME integrates a MoE module into dense retrieval models for open-domain Q&A.
Specializers focus on contextualizing queries for specific domains, improving model performance.
Experimental Analysis:
Extensive experiments show significant performance improvements with DESIRE-ME integration.
Results demonstrate enhanced ranking quality and adaptability to new datasets.
Conclusions:
DESIRE-ME enhances state-of-the-art dense retrieval models and generalizes well in zero-shot scenarios.
Future work includes optimizing neural architectures and exploring domain-specific query expansion.
Statystyki
提案されたDESIRE-MEモデルは、最大12%のNDCG@10および22%のP@1で密な検索モデルのパフォーマンスを向上させました。