Multimodal Representation Learning Impact on Zero-Shot Retrieval Effectiveness

Zero-shot evaluation of information retrieval (IR) models is often performed using BEIR; a large and heterogeneous benchmark composed of multiple datasets, covering different retrieval tasks across various domains. Although BEIR has become a standard benchmark for the zero-shot setup, its exclusively English content reduces its utility for underrepresented languages in IR, including Dutch. To address this limitation and encourage the development of Dutch IR models, we introduce BEIR-NL by automatically translating the publicly accessible BEIR datasets into Dutch. Using BEIR-NL, we evaluated a

Research goal: What is the impact of multimodal representation learning on zero-shot retrieval effectiveness when dense retrievers are pre-trained on both text and image data (e.g., CLIP-like models) compared to text-only models, evaluated using MRR on MS MARCO and BEIR datasets?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.